From 7f1938321cf9153bc9b46ceea4c5cd51c841e168 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Florentin=20D=C3=B6rre?= Date: Thu, 13 Nov 2025 15:50:25 +0100 Subject: [PATCH 1/2] Support linking to pandas, neo4j and co in sphinx works out of the box with autodoc. --- doc/sphinx/source/conf.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/doc/sphinx/source/conf.py b/doc/sphinx/source/conf.py index befe04f30..6fca87e84 100644 --- a/doc/sphinx/source/conf.py +++ b/doc/sphinx/source/conf.py @@ -23,6 +23,7 @@ extensions = [ "sphinx.ext.autodoc", # include docs from docstrings + "sphinx.ext.intersphinx", # link to other projects' documentation such as neo4j driver or pyArrow "enum_tools.autoenum", # specialised autoclass for enums "sphinx.ext.napoleon", # Support for NumPy and Google style docstrings "sphinxcontrib.autodoc_pydantic", # Support for Pydantic models @@ -60,6 +61,17 @@ def setup(app): # type: ignore app.add_js_file("js/deprecated.js", loading_method="defer") +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), + "neo4j": ("https://neo4j.com/docs/api/python-driver/current/", None), + "dateutil": ("https://dateutil.readthedocs.io/en/stable/", None), + "numpy": ("https://numpy.org/doc/stable/", None), + "pandas": ("https://pandas.pydata.org/docs/", None), + "pyarrow": ("https://arrow.apache.org/docs/", None), + "networkx": ("https://networkx.org/documentation/stable/", None), + "nx": ("https://networkx.org/documentation/stable/", None), +} + rst_epilog = """ .. |api-version| replace:: {versionnum} """.format( From 648ecf1c38d1c751ead7204adeca978ff9ad4ace Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Florentin=20D=C3=B6rre?= Date: Thu, 13 Nov 2025 16:04:37 +0100 Subject: [PATCH 2/2] Link to pandas and nx classes in typed-out ref docs --- doc/sphinx/algorithms.json | 880 +++++++++--------- doc/sphinx/source/algorithms.rst | 880 +++++++++--------- doc/sphinx/source/conf.py | 1 - doc/sphinx/source/graph.rst | 84 +- doc/sphinx/source/misc.rst | 18 +- doc/sphinx/source/ml.rst | 98 +- doc/sphinx/source/model.rst | 32 +- .../source/themes/neo4j/static/css/neo4j.css | 18 +- 8 files changed, 1009 insertions(+), 1002 deletions(-) diff --git a/doc/sphinx/algorithms.json b/doc/sphinx/algorithms.json index 1aa1cd5e1..d5aa8e451 100644 --- a/doc/sphinx/algorithms.json +++ b/doc/sphinx/algorithms.json @@ -3,7 +3,7 @@ "function": { "name": "gds.allShortestPaths.delta.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Delta Stepping shortest path algorithm computes the shortest (weighted) path\nbetween one node and any other node in the graph. The computation is run multi-threaded." }, @@ -11,15 +11,15 @@ "function": { "name": "gds.allShortestPaths.delta.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, - "description": "Returns an estimation of the memory consumption for allShortestPaths.dealta.mutate." + "description": "Returns an estimation of the memory consumption for allShortestPaths.delta.mutate." }, { "function": { "name": "gds.allShortestPaths.delta.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Delta Stepping shortest path algorithm computes the shortest (weighted) path\nbetween one node and any other node in the graph. The computation is run multi-threaded" }, @@ -27,7 +27,7 @@ "function": { "name": "gds.allShortestPaths.delta.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for allShortestPaths.delta.stats." }, @@ -35,7 +35,7 @@ "function": { "name": "gds.allShortestPaths.delta.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Delta Stepping shortest path algorithm computes the shortest (weighted) path\nbetween one node and any other node in the graph. The computation is run multi-threaded." }, @@ -43,15 +43,15 @@ "function": { "name": "gds.allShortestPaths.delta.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, - "description": "Returns an estimation of the memory consumption for allShortestPaths.delta.strema." + "description": "Returns an estimation of the memory consumption for allShortestPaths.delta.stream." }, { "function": { "name": "gds.allShortestPaths.delta.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Delta Stepping shortest path algorithm computes the shortest (weighted) path\nbetween one node and any other node in the graph. The computation is run multi-threaded." }, @@ -59,7 +59,7 @@ "function": { "name": "gds.allShortestPaths.delta.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -67,7 +67,7 @@ "function": { "name": "gds.allShortestPaths.dijkstra.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Dijkstra shortest path algorithm computes the shortest (weighted) path\nbetween one node and any other node in the graph." }, @@ -75,7 +75,7 @@ "function": { "name": "gds.allShortestPaths.dijkstra.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -83,7 +83,7 @@ "function": { "name": "gds.allShortestPaths.dijkstra.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Dijkstra shortest path algorithm computes the shortest (weighted) path\nbetween one node and any other node in the graph." }, @@ -91,7 +91,7 @@ "function": { "name": "gds.allShortestPaths.dijkstra.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -99,7 +99,7 @@ "function": { "name": "gds.allShortestPaths.dijkstra.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Dijkstra shortest path algorithm computes the shortest (weighted) path\nbetween one node and any other node in the graph." }, @@ -107,7 +107,7 @@ "function": { "name": "gds.allShortestPaths.dijkstra.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -115,7 +115,7 @@ "function": { "name": "gds.allShortestPaths.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The All Pairs Shortest Path (APSP) calculates the shortest (weighted) path\nbetween all pairs of nodes." }, @@ -123,7 +123,7 @@ "function": { "name": "gds.allShortestPaths.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -131,7 +131,7 @@ "function": { "name": "gds.alpha.allShortestPaths.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The All Pairs Shortest Path (APSP) calculates the shortest (weighted) path\nbetween all pairs of nodes.", "deprecated": { @@ -143,7 +143,7 @@ "function": { "name": "gds.alpha.closeness.harmonic.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Harmonic centrality is a way of detecting nodes that are able to spread information\nvery efficiently through a graph.", "deprecated": { @@ -155,7 +155,7 @@ "function": { "name": "gds.alpha.closeness.harmonic.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Harmonic centrality is a way of detecting nodes that are able to spread information\nvery efficiently through a graph.", "deprecated": { @@ -167,7 +167,7 @@ "function": { "name": "gds.alpha.conductance.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Evaluates a division of nodes into communities based on the proportion of relationships\nthat cross community boundaries." }, @@ -187,7 +187,7 @@ "function": { "name": "gds.alpha.hits.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes.", "deprecated": { @@ -199,7 +199,7 @@ "function": { "name": "gds.alpha.hits.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -211,7 +211,7 @@ "function": { "name": "gds.alpha.hits.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes.", "deprecated": { @@ -223,7 +223,7 @@ "function": { "name": "gds.alpha.hits.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -235,7 +235,7 @@ "function": { "name": "gds.alpha.hits.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes.", "deprecated": { @@ -247,7 +247,7 @@ "function": { "name": "gds.alpha.hits.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -259,7 +259,7 @@ "function": { "name": "gds.alpha.hits.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes.", "deprecated": { @@ -271,7 +271,7 @@ "function": { "name": "gds.alpha.hits.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -283,7 +283,7 @@ "function": { "name": "gds.alpha.kSpanningTree.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The K-spanning tree algorithm starts from a root node and returns a spanning tree with exactly k nodes", "deprecated": { @@ -295,7 +295,7 @@ "function": { "name": "gds.alpha.knn.filtered.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties.\nFiltered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively." }, @@ -303,7 +303,7 @@ "function": { "name": "gds.alpha.knn.filtered.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties.\nFiltered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively." }, @@ -311,7 +311,7 @@ "function": { "name": "gds.alpha.knn.filtered.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties.\nFiltered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively." }, @@ -319,7 +319,7 @@ "function": { "name": "gds.alpha.knn.filtered.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties.\nFiltered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively." }, @@ -327,7 +327,7 @@ "function": { "name": "gds.alpha.maxkcut.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Approximate Maximum k-cut maps each node into one of k disjoint communities\ntrying to maximize the sum of weights of relationships between these communities." }, @@ -335,7 +335,7 @@ "function": { "name": "gds.alpha.maxkcut.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Approximate Maximum k-cut maps each node into one of k disjoint communities\ntrying to maximize the sum of weights of relationships between these communities." }, @@ -343,7 +343,7 @@ "function": { "name": "gds.alpha.maxkcut.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Approximate Maximum k-cut maps each node into one of k disjoint communities\ntrying to maximize the sum of weights of relationships between these communities." }, @@ -351,7 +351,7 @@ "function": { "name": "gds.alpha.maxkcut.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Approximate Maximum k-cut maps each node into one of k disjoint communities\ntrying to maximize the sum of weights of relationships between these communities." }, @@ -359,21 +359,21 @@ "function": { "name": "gds.alpha.modularity.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" } }, { "function": { "name": "gds.alpha.modularity.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" } }, { "function": { "name": "gds.alpha.nodeSimilarity.filtered.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nThe algorithm computes pair-wise similarities based on Jaccard or Overlap metrics.\nThe filtered variant supports limiting which nodes to compare via source and target node filters." }, @@ -381,7 +381,7 @@ "function": { "name": "gds.alpha.nodeSimilarity.filtered.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -389,7 +389,7 @@ "function": { "name": "gds.alpha.nodeSimilarity.filtered.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nThe algorithm computes pair-wise similarities based on Jaccard or Overlap metrics.\nThe filtered variant supports limiting which nodes to compare via source and target node filters." }, @@ -397,7 +397,7 @@ "function": { "name": "gds.alpha.nodeSimilarity.filtered.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -405,7 +405,7 @@ "function": { "name": "gds.alpha.nodeSimilarity.filtered.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nThe algorithm computes pair-wise similarities based on Jaccard or Overlap metrics.\nThe filtered variant supports limiting which nodes to compare via source and target node filters." }, @@ -413,7 +413,7 @@ "function": { "name": "gds.alpha.nodeSimilarity.filtered.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -421,7 +421,7 @@ "function": { "name": "gds.alpha.nodeSimilarity.filtered.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nThe algorithm computes pair-wise similarities based on Jaccard or Overlap metrics.\nThe filtered variant supports limiting which nodes to compare via source and target node filters." }, @@ -429,7 +429,7 @@ "function": { "name": "gds.alpha.nodeSimilarity.filtered.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -437,7 +437,7 @@ "function": { "name": "gds.alpha.scaleProperties.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Scale node properties", "deprecated": { @@ -449,7 +449,7 @@ "function": { "name": "gds.alpha.scaleProperties.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Scale node properties", "deprecated": { @@ -461,7 +461,7 @@ "function": { "name": "gds.alpha.scc.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The SCC algorithm finds sets of connected nodes in an directed graph,\nwhere all nodes in the same set form a connected component." }, @@ -469,7 +469,7 @@ "function": { "name": "gds.alpha.scc.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The SCC algorithm finds sets of connected nodes in an directed graph,\nwhere all nodes in the same set form a connected component." }, @@ -477,7 +477,7 @@ "function": { "name": "gds.alpha.sllpa.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph.", "deprecated": { @@ -489,7 +489,7 @@ "function": { "name": "gds.alpha.sllpa.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -501,7 +501,7 @@ "function": { "name": "gds.alpha.sllpa.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph.", "deprecated": { @@ -513,7 +513,7 @@ "function": { "name": "gds.alpha.sllpa.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -525,7 +525,7 @@ "function": { "name": "gds.alpha.sllpa.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph.", "deprecated": { @@ -537,7 +537,7 @@ "function": { "name": "gds.alpha.sllpa.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -549,7 +549,7 @@ "function": { "name": "gds.alpha.sllpa.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph.", "deprecated": { @@ -561,7 +561,7 @@ "function": { "name": "gds.alpha.sllpa.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -573,7 +573,7 @@ "function": { "name": "gds.alpha.triangles", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Triangles streams the nodeIds of each triangle in the graph.", "deprecated": { @@ -585,7 +585,7 @@ "function": { "name": "gds.articleRank.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Article Rank is a variant of the Page Rank algorithm, which measures the transitive influence or connectivity of nodes." }, @@ -593,7 +593,7 @@ "function": { "name": "gds.articleRank.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -601,7 +601,7 @@ "function": { "name": "gds.articleRank.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Executes the algorithm and returns result statistics without writing the result to Neo4j." }, @@ -609,7 +609,7 @@ "function": { "name": "gds.articleRank.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -617,7 +617,7 @@ "function": { "name": "gds.articleRank.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Article Rank is a variant of the Page Rank algorithm, which measures the transitive influence or connectivity of nodes." }, @@ -625,7 +625,7 @@ "function": { "name": "gds.articleRank.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -633,7 +633,7 @@ "function": { "name": "gds.articleRank.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Article Rank is a variant of the Page Rank algorithm, which measures the transitive influence or connectivity of nodes." }, @@ -641,7 +641,7 @@ "function": { "name": "gds.articleRank.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -649,7 +649,7 @@ "function": { "name": "gds.articulationPoints.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Articulation Points is an algorithm that finds nodes that disconnect components if removed." }, @@ -657,7 +657,7 @@ "function": { "name": "gds.articulationPoints.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -666,7 +666,7 @@ "function": { "name": "gds.articulationPoints.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Articulation Points is an algorithm that finds nodes that disconnect components if removed." }, @@ -674,7 +674,7 @@ "function": { "name": "gds.articulationPoints.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -682,7 +682,7 @@ "function": { "name": "gds.articulationPoints.stream", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Articulation Points is an algorithm that finds nodes that disconnect components if removed." }, @@ -690,7 +690,7 @@ "function": { "name": "gds.articulationPoints.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -698,7 +698,7 @@ "function": { "name": "gds.articulationPoints.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Articulation Points is an algorithm that finds nodes that disconnect components if removed." }, @@ -706,7 +706,7 @@ "function": { "name": "gds.articulationPoints.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -714,7 +714,7 @@ "function": { "name": "gds.bellmanFord.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Bellman-Ford shortest path algorithm computes the shortest (weighted) path between one node\nand any other node in the graph without negative cycles." }, @@ -722,7 +722,7 @@ "function": { "name": "gds.bellmanFord.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -730,7 +730,7 @@ "function": { "name": "gds.bellmanFord.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Bellman-Ford shortest path algorithm computes the shortest (weighted) path between one node\nand any other node in the graph without negative cycles." }, @@ -738,7 +738,7 @@ "function": { "name": "gds.bellmanFord.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -746,7 +746,7 @@ "function": { "name": "gds.bellmanFord.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Bellman-Ford shortest path algorithm computes the shortest (weighted) path between one node\nand any other node in the graph without negative cycles." }, @@ -754,7 +754,7 @@ "function": { "name": "gds.bellmanFord.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -762,7 +762,7 @@ "function": { "name": "gds.bellmanFord.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Bellman-Ford shortest path algorithm computes the shortest (weighted) path between one node\nand any other node in the graph without negative cycles." }, @@ -770,7 +770,7 @@ "function": { "name": "gds.bellmanFord.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -778,7 +778,7 @@ "function": { "name": "gds.beta.closeness.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph." }, @@ -786,7 +786,7 @@ "function": { "name": "gds.beta.closeness.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph." }, @@ -794,7 +794,7 @@ "function": { "name": "gds.beta.closeness.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph." }, @@ -802,7 +802,7 @@ "function": { "name": "gds.beta.closeness.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph." }, @@ -810,7 +810,7 @@ "function": { "name": "gds.beta.collapsePath.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Collapse Path algorithm is a traversal algorithm capable of creating relationships between the start\nand end nodes of a traversal" }, @@ -818,7 +818,7 @@ "function": { "name": "gds.beta.influenceMaximization.celf.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes\nthat maximize the expected spread of influence in the network." }, @@ -826,7 +826,7 @@ "function": { "name": "gds.beta.influenceMaximization.celf.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -834,7 +834,7 @@ "function": { "name": "gds.beta.influenceMaximization.celf.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Executes the algorithm and returns result statistics without writing the result to Neo4j." }, @@ -842,7 +842,7 @@ "function": { "name": "gds.beta.influenceMaximization.celf.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -850,7 +850,7 @@ "function": { "name": "gds.beta.influenceMaximization.celf.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes\nthat maximize the expected spread of influence in the network." }, @@ -858,7 +858,7 @@ "function": { "name": "gds.beta.influenceMaximization.celf.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes\nthat maximize the expected spread of influence in the network." }, @@ -866,7 +866,7 @@ "function": { "name": "gds.beta.influenceMaximization.celf.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes\nthat maximize the expected spread of influence in the network." }, @@ -874,7 +874,7 @@ "function": { "name": "gds.beta.influenceMaximization.celf.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -882,7 +882,7 @@ "function": { "name": "gds.beta.k1coloring.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The K-1 Coloring algorithm assigns a color to every node in the graph." }, @@ -890,7 +890,7 @@ "function": { "name": "gds.beta.k1coloring.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -898,7 +898,7 @@ "function": { "name": "gds.beta.k1coloring.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The K-1 Coloring algorithm assigns a color to every node in the graph." }, @@ -906,7 +906,7 @@ "function": { "name": "gds.beta.k1coloring.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -914,7 +914,7 @@ "function": { "name": "gds.beta.k1coloring.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The K-1 Coloring algorithm assigns a color to every node in the graph." }, @@ -922,7 +922,7 @@ "function": { "name": "gds.beta.k1coloring.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -930,7 +930,7 @@ "function": { "name": "gds.beta.k1coloring.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The K-1 Coloring algorithm assigns a color to every node in the graph." }, @@ -938,7 +938,7 @@ "function": { "name": "gds.beta.k1coloring.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -946,7 +946,7 @@ "function": { "name": "gds.beta.kmeans.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Kmeans algorithm clusters nodes into different communities based on Euclidean distance", "deprecated": { @@ -958,7 +958,7 @@ "function": { "name": "gds.beta.kmeans.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -970,7 +970,7 @@ "function": { "name": "gds.beta.kmeans.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Kmeans algorithm clusters nodes into different communities based on Euclidean distance", "deprecated": { @@ -982,7 +982,7 @@ "function": { "name": "gds.beta.kmeans.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -994,7 +994,7 @@ "function": { "name": "gds.beta.kmeans.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Kmeans algorithm clusters nodes into different communities based on Euclidean distance", "deprecated": { @@ -1006,7 +1006,7 @@ "function": { "name": "gds.beta.kmeans.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -1018,7 +1018,7 @@ "function": { "name": "gds.beta.kmeans.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Kmeans algorithm clusters nodes into different communities based on Euclidean distance", "deprecated": { @@ -1030,7 +1030,7 @@ "function": { "name": "gds.beta.kmeans.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure.", "deprecated": { @@ -1042,7 +1042,7 @@ "function": { "name": "gds.beta.leiden.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Leiden is a community detection algorithm, which guarantees that communities are well connected" }, @@ -1050,7 +1050,7 @@ "function": { "name": "gds.beta.leiden.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1058,7 +1058,7 @@ "function": { "name": "gds.beta.leiden.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Executes the algorithm and returns result statistics without writing the result to Neo4j." }, @@ -1066,7 +1066,7 @@ "function": { "name": "gds.beta.leiden.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1074,7 +1074,7 @@ "function": { "name": "gds.beta.leiden.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Leiden is a community detection algorithm, which guarantees that communities are well connected" }, @@ -1082,7 +1082,7 @@ "function": { "name": "gds.beta.leiden.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1090,7 +1090,7 @@ "function": { "name": "gds.beta.leiden.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Leiden is a community detection algorithm, which guarantees that communities are well connected" }, @@ -1098,7 +1098,7 @@ "function": { "name": "gds.beta.leiden.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1106,7 +1106,7 @@ "function": { "name": "gds.beta.modularityOptimization.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity." }, @@ -1114,7 +1114,7 @@ "function": { "name": "gds.beta.modularityOptimization.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1122,7 +1122,7 @@ "function": { "name": "gds.beta.modularityOptimization.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity." }, @@ -1130,7 +1130,7 @@ "function": { "name": "gds.beta.modularityOptimization.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1138,7 +1138,7 @@ "function": { "name": "gds.beta.modularityOptimization.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity." }, @@ -1146,7 +1146,7 @@ "function": { "name": "gds.beta.modularityOptimization.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1154,7 +1154,7 @@ "function": { "name": "gds.cliqueCounting.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Counts the number of cliques, of different size, each node is part of." }, @@ -1162,7 +1162,7 @@ "function": { "name": "gds.cliqueCounting.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1170,7 +1170,7 @@ "function": { "name": "gds.cliqueCounting.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Counts the number of cliques, of different size, each node is part of." }, @@ -1178,7 +1178,7 @@ "function": { "name": "gds.cliqueCounting.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1186,7 +1186,7 @@ "function": { "name": "gds.cliqueCounting.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Counts the number of cliques, of different size, in the entire graph." }, @@ -1194,7 +1194,7 @@ "function": { "name": "gds.cliqueCounting.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1202,7 +1202,7 @@ "function": { "name": "gds.cliqueCounting.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Counts the number of cliques, of different size, each node is part of." }, @@ -1210,7 +1210,7 @@ "function": { "name": "gds.cliqueCounting.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1218,7 +1218,7 @@ "function": { "name": "gds.scaleProperties.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Scale node properties" }, @@ -1226,7 +1226,7 @@ "function": { "name": "gds.scaleProperties.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1234,7 +1234,7 @@ "function": { "name": "gds.scaleProperties.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Scale node properties" }, @@ -1242,7 +1242,7 @@ "function": { "name": "gds.scaleProperties.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1250,7 +1250,7 @@ "function": { "name": "gds.scaleProperties.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Scale node properties" }, @@ -1258,7 +1258,7 @@ "function": { "name": "gds.scaleProperties.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1266,7 +1266,7 @@ "function": { "name": "gds.scaleProperties.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Scale node properties" }, @@ -1274,7 +1274,7 @@ "function": { "name": "gds.scaleProperties.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1282,7 +1282,7 @@ "function": { "name": "gds.scc.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component." }, @@ -1290,7 +1290,7 @@ "function": { "name": "gds.scc.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for SCC." }, @@ -1298,7 +1298,7 @@ "function": { "name": "gds.scc.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component." }, @@ -1306,7 +1306,7 @@ "function": { "name": "gds.scc.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for SCC." }, @@ -1314,7 +1314,7 @@ "function": { "name": "gds.scc.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component." }, @@ -1322,7 +1322,7 @@ "function": { "name": "gds.scc.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for SCC." }, @@ -1330,7 +1330,7 @@ "function": { "name": "gds.scc.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component." }, @@ -1338,7 +1338,7 @@ "function": { "name": "gds.scc.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for SCC." }, @@ -1346,7 +1346,7 @@ "function": { "name": "gds.beta.spanningTree.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The spanning tree algorithm visits all nodes that are in the same connected component as the starting node,\nand returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized." }, @@ -1354,7 +1354,7 @@ "function": { "name": "gds.beta.spanningTree.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1362,7 +1362,7 @@ "function": { "name": "gds.beta.spanningTree.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The spanning tree algorithm visits all nodes that are in the same connected component as the starting node,\nand returns a spanning tree of all nodes in the component\nwhere the total weight of the relationships is either minimized or maximized." }, @@ -1370,7 +1370,7 @@ "function": { "name": "gds.beta.spanningTree.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1378,7 +1378,7 @@ "function": { "name": "gds.beta.spanningTree.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The spanning tree algorithm visits all nodes that are in the same connected component as the starting node,\nand returns a spanning tree of all nodes in the component\nwhere the total weight of the relationships is either minimized or maximized." }, @@ -1386,7 +1386,7 @@ "function": { "name": "gds.beta.spanningTree.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1394,7 +1394,7 @@ "function": { "name": "gds.beta.spanningTree.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The spanning tree algorithm visits all nodes that are in the same connected component as the starting node,\nand returns a spanning tree of all nodes in the component\nwhere the total weight of the relationships is either minimized or maximized." }, @@ -1402,7 +1402,7 @@ "function": { "name": "gds.beta.spanningTree.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1410,7 +1410,7 @@ "function": { "name": "gds.beta.steinerTree.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The steiner tree algorithm accepts a source node, as well as a list of target nodes.\nIt then attempts to find a spanning tree where there is a path from the source node to each target node,\nsuch that the total weight of the relationships is as low as possible." }, @@ -1418,7 +1418,7 @@ "function": { "name": "gds.beta.steinerTree.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The steiner tree algorithm accepts a source node, as well as a list of target nodes.\nIt then attempts to find a spanning tree where there is a path from the source node to each target node,\nsuch that the total weight of the relationships is as low as possible." }, @@ -1426,7 +1426,7 @@ "function": { "name": "gds.beta.steinerTree.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The steiner tree algorithm accepts a source node, as well as a list of target nodes.\nIt then attempts to find a spanning tree where there is a path from the source node to each target node,\nsuch that the total weight of the relationships is as low as possible." }, @@ -1434,7 +1434,7 @@ "function": { "name": "gds.beta.steinerTree.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The steiner tree algorithm accepts a source node, as well as a list of target nodes.\nIt then attempts to find a spanning tree where there is a path from the source node to each target node,\nsuch that the total weight of the relationships is as low as possible." }, @@ -1442,7 +1442,7 @@ "function": { "name": "gds.betweenness.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Betweenness centrality measures the relative information flow that passes through a node." }, @@ -1450,7 +1450,7 @@ "function": { "name": "gds.betweenness.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Betweenness centrality measures the relative information flow that passes through a node." }, @@ -1458,7 +1458,7 @@ "function": { "name": "gds.betweenness.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Betweenness centrality measures the relative information flow that passes through a node." }, @@ -1466,7 +1466,7 @@ "function": { "name": "gds.betweenness.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Betweenness centrality measures the relative information flow that passes through a node." }, @@ -1474,7 +1474,7 @@ "function": { "name": "gds.betweenness.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Betweenness centrality measures the relative information flow that passes through a node." }, @@ -1482,7 +1482,7 @@ "function": { "name": "gds.betweenness.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Betweenness centrality measures the relative information flow that passes through a node." }, @@ -1490,7 +1490,7 @@ "function": { "name": "gds.betweenness.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Betweenness centrality measures the relative information flow that passes through a node." }, @@ -1498,7 +1498,7 @@ "function": { "name": "gds.betweenness.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Betweenness centrality measures the relative information flow that passes through a node." }, @@ -1506,7 +1506,7 @@ "function": { "name": "gds.bfs.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "BFS is a traversal algorithm, which explores all of the neighbor nodes at the present depth\nprior to moving on to the nodes at the next depth level." }, @@ -1514,7 +1514,7 @@ "function": { "name": "gds.bfs.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1522,7 +1522,7 @@ "function": { "name": "gds.bfs.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "BFS is a traversal algorithm, which explores all of the neighbor nodes at the present depth\nprior to moving on to the nodes at the next depth level." }, @@ -1530,7 +1530,7 @@ "function": { "name": "gds.bfs.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1538,7 +1538,7 @@ "function": { "name": "gds.bfs.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "BFS is a traversal algorithm, which explores all of the neighbor nodes at the present depth\nprior to moving on to the nodes at the next depth level." }, @@ -1546,7 +1546,7 @@ "function": { "name": "gds.bfs.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "BFS is a traversal algorithm, which explores all of the neighbor nodes at the present depth\nprior to moving on to the nodes at the next depth level." }, @@ -1554,7 +1554,7 @@ "function": { "name": "gds.bridges.stream", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "An algorithm to find Bridge edges in a graph." }, @@ -1562,7 +1562,7 @@ "function": { "name": "gds.bridges.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1570,7 +1570,7 @@ "function": { "name": "gds.closeness.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph." }, @@ -1578,7 +1578,7 @@ "function": { "name": "gds.closeness.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1586,7 +1586,7 @@ "function": { "name": "gds.closeness.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph." }, @@ -1594,7 +1594,7 @@ "function": { "name": "gds.closeness.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1602,7 +1602,7 @@ "function": { "name": "gds.closeness.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph." }, @@ -1610,7 +1610,7 @@ "function": { "name": "gds.closeness.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1618,7 +1618,7 @@ "function": { "name": "gds.closeness.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph." }, @@ -1626,7 +1626,7 @@ "function": { "name": "gds.closeness.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1634,7 +1634,7 @@ "function": { "name": "gds.closeness.harmonic.mutate", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Harmonic centrality is a way of detecting nodes that are able to spread information\nvery efficiently through a graph." }, @@ -1642,7 +1642,7 @@ "function": { "name": "gds.closeness.harmonic.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1650,7 +1650,7 @@ "function": { "name": "gds.closeness.harmonic.stats", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Harmonic centrality is a way of detecting nodes that are able to spread information\nvery efficiently through a graph." }, @@ -1658,7 +1658,7 @@ "function": { "name": "gds.closeness.harmonic.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1666,7 +1666,7 @@ "function": { "name": "gds.closeness.harmonic.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Harmonic centrality is a way of detecting nodes that are able to spread information\nvery efficiently through a graph." }, @@ -1674,7 +1674,7 @@ "function": { "name": "gds.closeness.harmonic.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1682,7 +1682,7 @@ "function": { "name": "gds.closeness.harmonic.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Harmonic centrality is a way of detecting nodes that are able to spread information\nvery efficiently through a graph." }, @@ -1690,7 +1690,7 @@ "function": { "name": "gds.closeness.harmonic.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1698,7 +1698,7 @@ "function": { "name": "gds.collapsePath.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Collapse Path algorithm is a traversal algorithm capable of creating relationships between the start\nand end nodes of a traversal" }, @@ -1706,7 +1706,7 @@ "function": { "name": "gds.conductance.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Evaluates a division of nodes into communities based on the proportion of relationships\nthat cross community boundaries." }, @@ -1714,7 +1714,7 @@ "function": { "name": "gds.dag.topologicalSort.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Returns a topological ordering of the nodes in a directed acyclic graph (DAG)." }, @@ -1722,7 +1722,7 @@ "function": { "name": "gds.dag.longestPath.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Finds the longest path that leads to a node in a directed acyclic graph (DAG)." }, @@ -1730,7 +1730,7 @@ "function": { "name": "gds.degree.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Degree centrality measures the number of incoming and outgoing relationships from a node." }, @@ -1738,7 +1738,7 @@ "function": { "name": "gds.degree.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Degree centrality measures the number of incoming and outgoing relationships from a node." }, @@ -1746,7 +1746,7 @@ "function": { "name": "gds.degree.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Degree centrality measures the number of incoming and outgoing relationships from a node." }, @@ -1754,7 +1754,7 @@ "function": { "name": "gds.degree.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Degree centrality measures the number of incoming and outgoing relationships from a node." }, @@ -1762,7 +1762,7 @@ "function": { "name": "gds.degree.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Degree centrality measures the number of incoming and outgoing relationships from a node." }, @@ -1770,7 +1770,7 @@ "function": { "name": "gds.degree.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Degree centrality measures the number of incoming and outgoing relationships from a node." }, @@ -1778,7 +1778,7 @@ "function": { "name": "gds.degree.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Degree centrality measures the number of incoming and outgoing relationships from a node." }, @@ -1786,7 +1786,7 @@ "function": { "name": "gds.degree.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Degree centrality measures the number of incoming and outgoing relationships from a node." }, @@ -1794,7 +1794,7 @@ "function": { "name": "gds.dfs.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures.\nThe algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph)\nand explores as far as possible along each branch before backtracking." }, @@ -1802,7 +1802,7 @@ "function": { "name": "gds.dfs.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1810,7 +1810,7 @@ "function": { "name": "gds.dfs.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures.\nThe algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph)\nand explores as far as possible along each branch before backtracking." }, @@ -1818,7 +1818,7 @@ "function": { "name": "gds.dfs.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures.\nThe algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph)\nand explores as far as possible along each branch before backtracking." }, @@ -1826,7 +1826,7 @@ "function": { "name": "gds.eigenvector.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Eigenvector Centrality is an algorithm that measures the transitive influence or connectivity of nodes." }, @@ -1834,7 +1834,7 @@ "function": { "name": "gds.eigenvector.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1842,7 +1842,7 @@ "function": { "name": "gds.eigenvector.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Eigenvector Centrality is an algorithm that measures the transitive influence or connectivity of nodes." }, @@ -1850,7 +1850,7 @@ "function": { "name": "gds.eigenvector.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1858,7 +1858,7 @@ "function": { "name": "gds.eigenvector.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Eigenvector Centrality is an algorithm that measures the transitive influence or connectivity of nodes." }, @@ -1866,7 +1866,7 @@ "function": { "name": "gds.eigenvector.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1874,7 +1874,7 @@ "function": { "name": "gds.eigenvector.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Eigenvector Centrality is an algorithm that measures the transitive influence or connectivity of nodes." }, @@ -1882,7 +1882,7 @@ "function": { "name": "gds.eigenvector.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1898,7 +1898,7 @@ "function": { "name": "gds.graph.sample.cnarw.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1914,7 +1914,7 @@ "function": { "name": "gds.hdbscan.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hierarchical Density-Based Spatial Clustering of Applications with Noise" }, @@ -1922,7 +1922,7 @@ "function": { "name": "gds.hdbscan.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1930,7 +1930,7 @@ "function": { "name": "gds.hdbscan.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hierarchical Density-Based Spatial Clustering of Applications with Noise" }, @@ -1938,7 +1938,7 @@ "function": { "name": "gds.hdbscan.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1946,7 +1946,7 @@ "function": { "name": "gds.hdbscan.stream", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hierarchical Density-Based Spatial Clustering of Applications with Noise" }, @@ -1954,7 +1954,7 @@ "function": { "name": "gds.hdbscan.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1962,7 +1962,7 @@ "function": { "name": "gds.hdbscan.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hierarchical Density-Based Spatial Clustering of Applications with Noise" }, @@ -1970,7 +1970,7 @@ "function": { "name": "gds.hdbscan.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1978,7 +1978,7 @@ "function": { "name": "gds.hits.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes." }, @@ -1986,7 +1986,7 @@ "function": { "name": "gds.hits.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -1994,7 +1994,7 @@ "function": { "name": "gds.hits.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes." }, @@ -2002,7 +2002,7 @@ "function": { "name": "gds.hits.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2010,7 +2010,7 @@ "function": { "name": "gds.hits.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes." }, @@ -2018,7 +2018,7 @@ "function": { "name": "gds.hits.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2026,7 +2026,7 @@ "function": { "name": "gds.hits.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes." }, @@ -2034,7 +2034,7 @@ "function": { "name": "gds.hits.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2042,7 +2042,7 @@ "function": { "name": "gds.influenceMaximization.celf.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes\nthat maximize the expected spread of influence in the network." }, @@ -2050,7 +2050,7 @@ "function": { "name": "gds.influenceMaximization.celf.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2058,7 +2058,7 @@ "function": { "name": "gds.influenceMaximization.celf.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Executes the algorithm and returns result statistics without writing the result to Neo4j." }, @@ -2066,7 +2066,7 @@ "function": { "name": "gds.influenceMaximization.celf.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2074,7 +2074,7 @@ "function": { "name": "gds.influenceMaximization.celf.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes\nthat maximize the expected spread of influence in the network." }, @@ -2082,7 +2082,7 @@ "function": { "name": "gds.influenceMaximization.celf.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes\nthat maximize the expected spread of influence in the network." }, @@ -2090,7 +2090,7 @@ "function": { "name": "gds.influenceMaximization.celf.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes\nthat maximize the expected spread of influence in the network." }, @@ -2098,7 +2098,7 @@ "function": { "name": "gds.influenceMaximization.celf.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2106,7 +2106,7 @@ "function": { "name": "gds.kmeans.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Kmeans algorithm clusters nodes into different communities based on Euclidean distance" }, @@ -2114,7 +2114,7 @@ "function": { "name": "gds.kmeans.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2122,7 +2122,7 @@ "function": { "name": "gds.kmeans.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Kmeans algorithm clusters nodes into different communities based on Euclidean distance" }, @@ -2130,7 +2130,7 @@ "function": { "name": "gds.kmeans.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2138,7 +2138,7 @@ "function": { "name": "gds.kmeans.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Kmeans algorithm clusters nodes into different communities based on Euclidean distance" }, @@ -2146,7 +2146,7 @@ "function": { "name": "gds.kmeans.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2154,7 +2154,7 @@ "function": { "name": "gds.kmeans.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Kmeans algorithm clusters nodes into different communities based on Euclidean distance" }, @@ -2162,7 +2162,7 @@ "function": { "name": "gds.kmeans.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2170,7 +2170,7 @@ "function": { "name": "gds.k1coloring.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The K-1 Coloring algorithm assigns a color to every node in the graph." }, @@ -2178,7 +2178,7 @@ "function": { "name": "gds.k1coloring.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2186,7 +2186,7 @@ "function": { "name": "gds.k1coloring.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The K-1 Coloring algorithm assigns a color to every node in the graph." }, @@ -2194,7 +2194,7 @@ "function": { "name": "gds.k1coloring.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2202,7 +2202,7 @@ "function": { "name": "gds.k1coloring.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The K-1 Coloring algorithm assigns a color to every node in the graph." }, @@ -2210,7 +2210,7 @@ "function": { "name": "gds.k1coloring.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2218,7 +2218,7 @@ "function": { "name": "gds.k1coloring.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The K-1 Coloring algorithm assigns a color to every node in the graph." }, @@ -2226,7 +2226,7 @@ "function": { "name": "gds.k1coloring.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2234,7 +2234,7 @@ "function": { "name": "gds.kcore.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Computes the k-core values in a network" }, @@ -2242,7 +2242,7 @@ "function": { "name": "gds.kcore.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2250,7 +2250,7 @@ "function": { "name": "gds.kcore.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Computes the k-core values in a network" }, @@ -2258,7 +2258,7 @@ "function": { "name": "gds.kcore.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2266,7 +2266,7 @@ "function": { "name": "gds.kcore.stream", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Computes the k-core values in a network" }, @@ -2274,7 +2274,7 @@ "function": { "name": "gds.kcore.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2282,7 +2282,7 @@ "function": { "name": "gds.kcore.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Computes the k-core values in a network" }, @@ -2290,7 +2290,7 @@ "function": { "name": "gds.kcore.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2298,7 +2298,7 @@ "function": { "name": "gds.knn.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties" }, @@ -2306,7 +2306,7 @@ "function": { "name": "gds.knn.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2314,7 +2314,7 @@ "function": { "name": "gds.knn.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties" }, @@ -2322,7 +2322,7 @@ "function": { "name": "gds.knn.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2330,7 +2330,7 @@ "function": { "name": "gds.knn.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties" }, @@ -2338,7 +2338,7 @@ "function": { "name": "gds.knn.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2346,7 +2346,7 @@ "function": { "name": "gds.knn.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties" }, @@ -2354,7 +2354,7 @@ "function": { "name": "gds.knn.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2362,15 +2362,15 @@ "function": { "name": "gds.knn.filtered.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, - "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties.\nFiltered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively.\n\n.. py:function:: gds.knn.filtered.mutate.estimate(G: Graph, **config: Any) -> Series[Any]\n\nReturns an estimation of the memory consumption for that procedure." + "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties.\nFiltered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively.\n\n.. py:function:: gds.knn.filtered.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any]\n\nReturns an estimation of the memory consumption for that procedure." }, { "function": { "name": "gds.knn.filtered.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties.\nFiltered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively." }, @@ -2378,7 +2378,7 @@ "function": { "name": "gds.knn.filtered.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2386,7 +2386,7 @@ "function": { "name": "gds.knn.filtered.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties.\nFiltered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively." }, @@ -2394,7 +2394,7 @@ "function": { "name": "gds.knn.filtered.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2402,7 +2402,7 @@ "function": { "name": "gds.knn.filtered.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance\nbetween two nodes is among the k nearest distances compared to other nodes.\nKNN computes distances based on the similarity of node properties.\nFiltered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively." }, @@ -2410,7 +2410,7 @@ "function": { "name": "gds.knn.filtered.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2418,7 +2418,7 @@ "function": { "name": "gds.kSpanningTree.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The K-spanning tree algorithm starts from a root node and returns a spanning tree with exactly k nodes" }, @@ -2426,7 +2426,7 @@ "function": { "name": "gds.labelPropagation.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Label Propagation algorithm is a fast algorithm for finding communities in a graph." }, @@ -2434,7 +2434,7 @@ "function": { "name": "gds.labelPropagation.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2442,7 +2442,7 @@ "function": { "name": "gds.labelPropagation.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Label Propagation algorithm is a fast algorithm for finding communities in a graph." }, @@ -2450,7 +2450,7 @@ "function": { "name": "gds.labelPropagation.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2458,7 +2458,7 @@ "function": { "name": "gds.labelPropagation.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Label Propagation algorithm is a fast algorithm for finding communities in a graph." }, @@ -2466,7 +2466,7 @@ "function": { "name": "gds.labelPropagation.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2474,7 +2474,7 @@ "function": { "name": "gds.labelPropagation.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Label Propagation algorithm is a fast algorithm for finding communities in a graph." }, @@ -2482,7 +2482,7 @@ "function": { "name": "gds.labelPropagation.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2490,7 +2490,7 @@ "function": { "name": "gds.leiden.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Leiden is a community detection algorithm, which guarantees that communities are well connected" }, @@ -2498,7 +2498,7 @@ "function": { "name": "gds.leiden.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2506,7 +2506,7 @@ "function": { "name": "gds.leiden.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Executes the algorithm and returns result statistics without writing the result to Neo4j." }, @@ -2514,7 +2514,7 @@ "function": { "name": "gds.leiden.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2522,7 +2522,7 @@ "function": { "name": "gds.leiden.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Leiden is a community detection algorithm, which guarantees that communities are well connected" }, @@ -2530,7 +2530,7 @@ "function": { "name": "gds.leiden.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2538,7 +2538,7 @@ "function": { "name": "gds.leiden.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Leiden is a community detection algorithm, which guarantees that communities are well connected" }, @@ -2546,7 +2546,7 @@ "function": { "name": "gds.leiden.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2554,7 +2554,7 @@ "function": { "name": "gds.localClusteringCoefficient.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The local clustering coefficient is a metric quantifying how connected the neighborhood of a node is." }, @@ -2562,7 +2562,7 @@ "function": { "name": "gds.localClusteringCoefficient.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2570,7 +2570,7 @@ "function": { "name": "gds.localClusteringCoefficient.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Executes the algorithm and returns result statistics without writing the result to Neo4j." }, @@ -2578,7 +2578,7 @@ "function": { "name": "gds.localClusteringCoefficient.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2586,7 +2586,7 @@ "function": { "name": "gds.localClusteringCoefficient.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The local clustering coefficient is a metric quantifying how connected the neighborhood of a node is." }, @@ -2594,7 +2594,7 @@ "function": { "name": "gds.localClusteringCoefficient.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2602,7 +2602,7 @@ "function": { "name": "gds.localClusteringCoefficient.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The local clustering coefficient is a metric quantifying how connected the neighborhood of a node is." }, @@ -2610,7 +2610,7 @@ "function": { "name": "gds.localClusteringCoefficient.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2618,7 +2618,7 @@ "function": { "name": "gds.louvain.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Louvain method for community detection is an algorithm for detecting communities in networks." }, @@ -2626,7 +2626,7 @@ "function": { "name": "gds.louvain.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2634,7 +2634,7 @@ "function": { "name": "gds.louvain.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Executes the algorithm and returns result statistics without writing the result to Neo4j." }, @@ -2642,7 +2642,7 @@ "function": { "name": "gds.louvain.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2650,7 +2650,7 @@ "function": { "name": "gds.louvain.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Louvain method for community detection is an algorithm for detecting communities in networks." }, @@ -2658,7 +2658,7 @@ "function": { "name": "gds.louvain.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2666,7 +2666,7 @@ "function": { "name": "gds.louvain.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Louvain method for community detection is an algorithm for detecting communities in networks." }, @@ -2674,7 +2674,7 @@ "function": { "name": "gds.louvain.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2682,7 +2682,7 @@ "function": { "name": "gds.maxFlow.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Computes the maximum flow between two nodes or sets of nodes in a graph." }, @@ -2690,7 +2690,7 @@ "function": { "name": "gds.maxFlow.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2698,7 +2698,7 @@ "function": { "name": "gds.maxFlow.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Computes the maximum flow between two nodes or sets of nodes in a graph." }, @@ -2706,7 +2706,7 @@ "function": { "name": "gds.maxFlow.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2714,7 +2714,7 @@ "function": { "name": "gds.maxFlow.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Computes the maximum flow between two nodes or sets of nodes in a graph." }, @@ -2722,7 +2722,7 @@ "function": { "name": "gds.maxFlow.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2730,7 +2730,7 @@ "function": { "name": "gds.maxFlow.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Computes the maximum flow between two nodes or sets of nodes in a graph." }, @@ -2738,7 +2738,7 @@ "function": { "name": "gds.maxFlow.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2746,7 +2746,7 @@ "function": { "name": "gds.maxkcut.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Approximate Maximum k-cut maps each node into one of k disjoint communities\ntrying to maximize the sum of weights of relationships between these communities." }, @@ -2754,7 +2754,7 @@ "function": { "name": "gds.maxkcut.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Approximate Maximum k-cut maps each node into one of k disjoint communities\ntrying to maximize the sum of weights of relationships between these communities." }, @@ -2762,7 +2762,7 @@ "function": { "name": "gds.maxkcut.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Approximate Maximum k-cut maps each node into one of k disjoint communities\ntrying to maximize the sum of weights of relationships between these communities." }, @@ -2770,7 +2770,7 @@ "function": { "name": "gds.maxkcut.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Approximate Maximum k-cut maps each node into one of k disjoint communities\ntrying to maximize the sum of weights of relationships between these communities." }, @@ -2778,35 +2778,35 @@ "function": { "name": "gds.modularity.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" } }, { "function": { "name": "gds.modularity.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" } }, { "function": { "name": "gds.modularity.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" } }, { "function": { "name": "gds.modularity.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" } }, { "function": { "name": "gds.modularityOptimization.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity." }, @@ -2814,7 +2814,7 @@ "function": { "name": "gds.modularityOptimization.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2822,7 +2822,7 @@ "function": { "name": "gds.modularityOptimization.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity." }, @@ -2830,7 +2830,7 @@ "function": { "name": "gds.modularityOptimization.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2838,7 +2838,7 @@ "function": { "name": "gds.modularityOptimization.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity." }, @@ -2846,7 +2846,7 @@ "function": { "name": "gds.modularityOptimization.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2854,7 +2854,7 @@ "function": { "name": "gds.modularityOptimization.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity." }, @@ -2862,7 +2862,7 @@ "function": { "name": "gds.modularityOptimization.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2870,7 +2870,7 @@ "function": { "name": "gds.nodeSimilarity.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nNode Similarity computes pair-wise similarities based on the Jaccard metric." }, @@ -2878,7 +2878,7 @@ "function": { "name": "gds.nodeSimilarity.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2886,7 +2886,7 @@ "function": { "name": "gds.nodeSimilarity.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nNode Similarity computes pair-wise similarities based on the Jaccard metric." }, @@ -2894,7 +2894,7 @@ "function": { "name": "gds.nodeSimilarity.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2902,7 +2902,7 @@ "function": { "name": "gds.nodeSimilarity.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nNode Similarity computes pair-wise similarities based on the Jaccard metric." }, @@ -2910,7 +2910,7 @@ "function": { "name": "gds.nodeSimilarity.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2918,7 +2918,7 @@ "function": { "name": "gds.nodeSimilarity.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nNode Similarity computes pair-wise similarities based on the Jaccard metric." }, @@ -2926,7 +2926,7 @@ "function": { "name": "gds.nodeSimilarity.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2934,7 +2934,7 @@ "function": { "name": "gds.nodeSimilarity.filtered.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nThe algorithm computes pair-wise similarities based on Jaccard or Overlap metrics.\nThe filtered variant supports limiting which nodes to compare via source and target node filters." }, @@ -2942,7 +2942,7 @@ "function": { "name": "gds.nodeSimilarity.filtered.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2950,7 +2950,7 @@ "function": { "name": "gds.nodeSimilarity.filtered.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nThe algorithm computes pair-wise similarities based on Jaccard or Overlap metrics.\nThe filtered variant supports limiting which nodes to compare via source and target node filters." }, @@ -2958,7 +2958,7 @@ "function": { "name": "gds.nodeSimilarity.filtered.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2966,7 +2966,7 @@ "function": { "name": "gds.nodeSimilarity.filtered.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nThe algorithm computes pair-wise similarities based on Jaccard or Overlap metrics.\nThe filtered variant supports limiting which nodes to compare via source and target node filters." }, @@ -2974,7 +2974,7 @@ "function": { "name": "gds.nodeSimilarity.filtered.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2982,7 +2982,7 @@ "function": { "name": "gds.nodeSimilarity.filtered.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to.\nTwo nodes are considered similar if they share many of the same neighbors.\nThe algorithm computes pair-wise similarities based on Jaccard or Overlap metrics.\nThe filtered variant supports limiting which nodes to compare via source and target node filters." }, @@ -2990,7 +2990,7 @@ "function": { "name": "gds.nodeSimilarity.filtered.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -2998,7 +2998,7 @@ "function": { "name": "gds.pageRank.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Page Rank is an algorithm that measures the transitive influence or connectivity of nodes." }, @@ -3006,7 +3006,7 @@ "function": { "name": "gds.pageRank.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3014,7 +3014,7 @@ "function": { "name": "gds.pageRank.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Executes the algorithm and returns result statistics without writing the result to Neo4j." }, @@ -3022,7 +3022,7 @@ "function": { "name": "gds.pageRank.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3030,7 +3030,7 @@ "function": { "name": "gds.pageRank.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Page Rank is an algorithm that measures the transitive influence or connectivity of nodes." }, @@ -3038,7 +3038,7 @@ "function": { "name": "gds.pageRank.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3046,7 +3046,7 @@ "function": { "name": "gds.pageRank.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Page Rank is an algorithm that measures the transitive influence or connectivity of nodes." }, @@ -3054,7 +3054,7 @@ "function": { "name": "gds.pageRank.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3062,7 +3062,7 @@ "function": { "name": "gds.prizeSteinerTree.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "An approximation algorithm for the prize collector steiner tree problem" }, @@ -3070,7 +3070,7 @@ "function": { "name": "gds.prizeSteinerTree.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3078,7 +3078,7 @@ "function": { "name": "gds.prizeSteinerTree.mutate", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "An approximation algorithm for the prize collector steiner tree problem" }, @@ -3086,7 +3086,7 @@ "function": { "name": "gds.prizeSteinerTree.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3094,7 +3094,7 @@ "function": { "name": "gds.prizeSteinerTree.stats", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "An approximation algorithm for the prize collector steiner tree problem" }, @@ -3102,7 +3102,7 @@ "function": { "name": "gds.prizeSteinerTree.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3110,7 +3110,7 @@ "function": { "name": "gds.prizeSteinerTree.write", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "An approximation algorithm for the prize collector steiner tree problem" }, @@ -3118,7 +3118,7 @@ "function": { "name": "gds.prizeSteinerTree.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3126,7 +3126,7 @@ "function": { "name": "gds.randomWalk.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Random Walk is an algorithm that provides random paths in a graph. It\u2019s similar to how a drunk person traverses a city. The mutate procedure produces the count a node occurs on a random walk." }, @@ -3134,7 +3134,7 @@ "function": { "name": "gds.randomWalk.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3142,7 +3142,7 @@ "function": { "name": "gds.randomWalk.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Random Walk is an algorithm that provides random paths in a graph. It\u2019s similar to how a drunk person traverses a city." }, @@ -3150,7 +3150,7 @@ "function": { "name": "gds.randomWalk.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3158,7 +3158,7 @@ "function": { "name": "gds.randomWalk.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Random Walk is an algorithm that provides random paths in a graph. It\u2019s similar to how a drunk person traverses a city." }, @@ -3166,7 +3166,7 @@ "function": { "name": "gds.randomWalk.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3174,7 +3174,7 @@ "function": { "name": "gds.shortestPath.astar.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The A* shortest path algorithm computes the shortest path between a pair of nodes. It uses the relationship weight\nproperty to compare path lengths. In addition,\nthis implementation uses the haversine distance as a heuristic to converge faster." }, @@ -3182,7 +3182,7 @@ "function": { "name": "gds.shortestPath.astar.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3190,7 +3190,7 @@ "function": { "name": "gds.shortestPath.astar.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The A* shortest path algorithm computes the shortest path between a pair of nodes. It uses the relationship weight\nproperty to compare path lengths. In addition,\nthis implementation uses the haversine distance as a heuristic to converge faster." }, @@ -3198,7 +3198,7 @@ "function": { "name": "gds.shortestPath.astar.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3206,7 +3206,7 @@ "function": { "name": "gds.shortestPath.astar.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The A* shortest path algorithm computes the shortest path between a pair of nodes. It uses the relationship weight\nproperty to compare path lengths. In addition,\nthis implementation uses the haversine distance as a heuristic to converge faster." }, @@ -3214,7 +3214,7 @@ "function": { "name": "gds.shortestPath.astar.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3222,7 +3222,7 @@ "function": { "name": "gds.shortestPath.dijkstra.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Dijkstra shortest path algorithm computes the shortest (weighted) path between a pair of nodes." }, @@ -3230,7 +3230,7 @@ "function": { "name": "gds.shortestPath.dijkstra.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3238,7 +3238,7 @@ "function": { "name": "gds.shortestPath.dijkstra.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Dijkstra shortest path algorithm computes the shortest (weighted) path between a pair of nodes." }, @@ -3246,7 +3246,7 @@ "function": { "name": "gds.shortestPath.dijkstra.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3254,7 +3254,7 @@ "function": { "name": "gds.shortestPath.dijkstra.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Dijkstra shortest path algorithm computes the shortest (weighted) path between a pair of nodes." }, @@ -3262,7 +3262,7 @@ "function": { "name": "gds.shortestPath.dijkstra.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3270,7 +3270,7 @@ "function": { "name": "gds.shortestPath.yens.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Yen's shortest path algorithm computes the k shortest (weighted) paths between a pair of nodes." }, @@ -3278,7 +3278,7 @@ "function": { "name": "gds.shortestPath.yens.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3286,7 +3286,7 @@ "function": { "name": "gds.shortestPath.yens.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Yen's shortest path algorithm computes the k shortest (weighted) paths between a pair of nodes." }, @@ -3294,7 +3294,7 @@ "function": { "name": "gds.shortestPath.yens.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3302,7 +3302,7 @@ "function": { "name": "gds.shortestPath.yens.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Yen's shortest path algorithm computes the k shortest (weighted) paths between a pair of nodes." }, @@ -3310,7 +3310,7 @@ "function": { "name": "gds.shortestPath.yens.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3318,7 +3318,7 @@ "function": { "name": "gds.sllpa.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph." }, @@ -3326,7 +3326,7 @@ "function": { "name": "gds.sllpa.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3334,7 +3334,7 @@ "function": { "name": "gds.sllpa.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph." }, @@ -3342,7 +3342,7 @@ "function": { "name": "gds.sllpa.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3350,7 +3350,7 @@ "function": { "name": "gds.sllpa.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph." }, @@ -3358,7 +3358,7 @@ "function": { "name": "gds.sllpa.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3366,7 +3366,7 @@ "function": { "name": "gds.sllpa.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph." }, @@ -3374,7 +3374,7 @@ "function": { "name": "gds.sllpa.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3382,7 +3382,7 @@ "function": { "name": "gds.spanningTree.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The spanning tree algorithm visits all nodes that are in the same connected component as the starting node,\nand returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized." }, @@ -3390,7 +3390,7 @@ "function": { "name": "gds.spanningTree.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3398,7 +3398,7 @@ "function": { "name": "gds.spanningTree.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The spanning tree algorithm visits all nodes that are in the same connected component as the starting node,\nand returns a spanning tree of all nodes in the component\nwhere the total weight of the relationships is either minimized or maximized." }, @@ -3406,7 +3406,7 @@ "function": { "name": "gds.spanningTree.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3414,7 +3414,7 @@ "function": { "name": "gds.spanningTree.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The spanning tree algorithm visits all nodes that are in the same connected component as the starting node,\nand returns a spanning tree of all nodes in the component\nwhere the total weight of the relationships is either minimized or maximized." }, @@ -3422,7 +3422,7 @@ "function": { "name": "gds.spanningTree.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3430,7 +3430,7 @@ "function": { "name": "gds.spanningTree.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The spanning tree algorithm visits all nodes that are in the same connected component as the starting node,\nand returns a spanning tree of all nodes in the component\nwhere the total weight of the relationships is either minimized or maximized." }, @@ -3438,7 +3438,7 @@ "function": { "name": "gds.spanningTree.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3446,7 +3446,7 @@ "function": { "name": "gds.steinerTree.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The steiner tree algorithm accepts a source node, as well as a list of target nodes.\nIt then attempts to find a spanning tree where there is a path from the source node to each target node,\nsuch that the total weight of the relationships is as low as possible." }, @@ -3454,7 +3454,7 @@ "function": { "name": "gds.steinerTree.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3462,7 +3462,7 @@ "function": { "name": "gds.steinerTree.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The steiner tree algorithm accepts a source node, as well as a list of target nodes.\nIt then attempts to find a spanning tree where there is a path from the source node to each target node,\nsuch that the total weight of the relationships is as low as possible." }, @@ -3470,7 +3470,7 @@ "function": { "name": "gds.steinerTree.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3478,7 +3478,7 @@ "function": { "name": "gds.steinerTree.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The steiner tree algorithm accepts a source node, as well as a list of target nodes.\nIt then attempts to find a spanning tree where there is a path from the source node to each target node,\nsuch that the total weight of the relationships is as low as possible." }, @@ -3486,7 +3486,7 @@ "function": { "name": "gds.steinerTree.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3494,7 +3494,7 @@ "function": { "name": "gds.steinerTree.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The steiner tree algorithm accepts a source node, as well as a list of target nodes.\nIt then attempts to find a spanning tree where there is a path from the source node to each target node,\nsuch that the total weight of the relationships is as low as possible." }, @@ -3502,7 +3502,7 @@ "function": { "name": "gds.steinerTree.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3510,7 +3510,7 @@ "function": { "name": "gds.triangleCount.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Triangle counting is a community detection graph algorithm that is used to\ndetermine the number of triangles passing through each node in the graph." }, @@ -3518,7 +3518,7 @@ "function": { "name": "gds.triangleCount.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3526,7 +3526,7 @@ "function": { "name": "gds.triangleCount.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Triangle counting is a community detection graph algorithm that is used to\ndetermine the number of triangles passing through each node in the graph." }, @@ -3534,7 +3534,7 @@ "function": { "name": "gds.triangleCount.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3542,7 +3542,7 @@ "function": { "name": "gds.triangleCount.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Triangle counting is a community detection graph algorithm that is used to\ndetermine the number of triangles passing through each node in the graph." }, @@ -3550,7 +3550,7 @@ "function": { "name": "gds.triangleCount.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3558,7 +3558,7 @@ "function": { "name": "gds.triangleCount.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Triangle counting is a community detection graph algorithm that is used to\ndetermine the number of triangles passing through each node in the graph." }, @@ -3566,7 +3566,7 @@ "function": { "name": "gds.triangleCount.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Triangle counting is a community detection graph algorithm that is used to\ndetermine the number of triangles passing through each node in the graph." }, @@ -3574,7 +3574,7 @@ "function": { "name": "gds.triangles", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "Triangles streams the nodeIds of each triangle in the graph." }, @@ -3582,7 +3582,7 @@ "function": { "name": "gds.wcc.mutate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The WCC algorithm finds sets of connected nodes in an undirected graph,\nwhere all nodes in the same set form a connected component." }, @@ -3590,7 +3590,7 @@ "function": { "name": "gds.wcc.mutate.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3598,7 +3598,7 @@ "function": { "name": "gds.wcc.stats", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Executes the algorithm and returns result statistics without writing the result to Neo4j." }, @@ -3606,7 +3606,7 @@ "function": { "name": "gds.wcc.stats.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3614,7 +3614,7 @@ "function": { "name": "gds.wcc.stream", "signature": "G: Graph, **config: Any", - "return_type": "DataFrame" + "return_type": "pandas.DataFrame" }, "description": "The WCC algorithm finds sets of connected nodes in an undirected graph,\nwhere all nodes in the same set form a connected component." }, @@ -3622,7 +3622,7 @@ "function": { "name": "gds.wcc.stream.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, @@ -3630,7 +3630,7 @@ "function": { "name": "gds.wcc.write", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "The WCC algorithm finds sets of connected nodes in an undirected graph,\nwhere all nodes in the same set form a connected component." }, @@ -3638,7 +3638,7 @@ "function": { "name": "gds.wcc.write.estimate", "signature": "G: Graph, **config: Any", - "return_type": "Series[Any]" + "return_type": "pandas.Series[Any]" }, "description": "Returns an estimation of the memory consumption for that procedure." }, diff --git a/doc/sphinx/source/algorithms.rst b/doc/sphinx/source/algorithms.rst index 6151841bc..f1da2142c 100644 --- a/doc/sphinx/source/algorithms.rst +++ b/doc/sphinx/source/algorithms.rst @@ -6,79 +6,79 @@ Algorithms procedures Listing of all algorithm procedures in the Neo4j Graph Data Science Python Client API. These all assume that an object of :class:`.GraphDataScience` is available as `gds`. -.. py:function:: gds.allShortestPaths.delta.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.delta.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Delta Stepping shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph. The computation is run multi-threaded. -.. py:function:: gds.allShortestPaths.delta.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.delta.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] - Returns an estimation of the memory consumption for allShortestPaths.dealta.mutate. + Returns an estimation of the memory consumption for allShortestPaths.delta.mutate. -.. py:function:: gds.allShortestPaths.delta.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.delta.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Delta Stepping shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph. The computation is run multi-threaded -.. py:function:: gds.allShortestPaths.delta.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.delta.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for allShortestPaths.delta.stats. -.. py:function:: gds.allShortestPaths.delta.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.allShortestPaths.delta.stream(G: Graph, **config: Any) -> pandas.DataFrame The Delta Stepping shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph. The computation is run multi-threaded. -.. py:function:: gds.allShortestPaths.delta.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.delta.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] - Returns an estimation of the memory consumption for allShortestPaths.delta.strema. + Returns an estimation of the memory consumption for allShortestPaths.delta.stream. -.. py:function:: gds.allShortestPaths.delta.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.delta.write(G: Graph, **config: Any) -> pandas.Series[Any] The Delta Stepping shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph. The computation is run multi-threaded. -.. py:function:: gds.allShortestPaths.delta.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.delta.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.allShortestPaths.dijkstra.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.dijkstra.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Dijkstra shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph. -.. py:function:: gds.allShortestPaths.dijkstra.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.dijkstra.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.allShortestPaths.dijkstra.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.allShortestPaths.dijkstra.stream(G: Graph, **config: Any) -> pandas.DataFrame The Dijkstra shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph. -.. py:function:: gds.allShortestPaths.dijkstra.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.dijkstra.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.allShortestPaths.dijkstra.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.dijkstra.write(G: Graph, **config: Any) -> pandas.Series[Any] The Dijkstra shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph. -.. py:function:: gds.allShortestPaths.dijkstra.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.dijkstra.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.allShortestPaths.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.allShortestPaths.stream(G: Graph, **config: Any) -> pandas.DataFrame The All Pairs Shortest Path (APSP) calculates the shortest (weighted) path between all pairs of nodes. -.. py:function:: gds.allShortestPaths.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.allShortestPaths.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.alpha.allShortestPaths.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.allShortestPaths.stream(G: Graph, **config: Any) -> pandas.DataFrame The All Pairs Shortest Path (APSP) calculates the shortest (weighted) path between all pairs of nodes. @@ -86,7 +86,7 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.allShortestPaths.stream` instead. -.. py:function:: gds.alpha.closeness.harmonic.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.closeness.harmonic.stream(G: Graph, **config: Any) -> pandas.DataFrame Harmonic centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. @@ -94,7 +94,7 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.closeness.harmonic.stream` instead. -.. py:function:: gds.alpha.closeness.harmonic.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.closeness.harmonic.write(G: Graph, **config: Any) -> pandas.Series[Any] Harmonic centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. @@ -102,7 +102,7 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.closeness.harmonic.write` instead. -.. py:function:: gds.alpha.conductance.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.conductance.stream(G: Graph, **config: Any) -> pandas.DataFrame Evaluates a division of nodes into communities based on the proportion of relationships that cross community boundaries. @@ -114,91 +114,91 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g .. deprecated:: 2.4.0 Since GDS server version 2.4.0 you should use the endpoint :func:`gds.graph.sample.rwr` instead. -.. py:function:: gds.alpha.hits.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.hits.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hits.mutate` instead. -.. py:function:: gds.alpha.hits.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.hits.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hits.mutate.estimate` instead. -.. py:function:: gds.alpha.hits.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.hits.stats(G: Graph, **config: Any) -> pandas.Series[Any] Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hits.stats` instead. -.. py:function:: gds.alpha.hits.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.hits.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hits.stats.estimate` instead. -.. py:function:: gds.alpha.hits.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.hits.stream(G: Graph, **config: Any) -> pandas.DataFrame Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hits.stream` instead. -.. py:function:: gds.alpha.hits.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.hits.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hits.stream.estimate` instead. -.. py:function:: gds.alpha.hits.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.hits.write(G: Graph, **config: Any) -> pandas.Series[Any] Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hits.write` instead. -.. py:function:: gds.alpha.hits.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.hits.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hits.write.estimate` instead. -.. py:function:: gds.alpha.kSpanningTree.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.kSpanningTree.write(G: Graph, **config: Any) -> pandas.Series[Any] The K-spanning tree algorithm starts from a root node and returns a spanning tree with exactly k nodes .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.kSpanningTree.write` instead. -.. py:function:: gds.alpha.knn.filtered.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.knn.filtered.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties. Filtered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively. -.. py:function:: gds.alpha.knn.filtered.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.knn.filtered.stats(G: Graph, **config: Any) -> pandas.Series[Any] The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties. Filtered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively. -.. py:function:: gds.alpha.knn.filtered.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.knn.filtered.stream(G: Graph, **config: Any) -> pandas.DataFrame The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties. Filtered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively. -.. py:function:: gds.alpha.knn.filtered.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.knn.filtered.write(G: Graph, **config: Any) -> pandas.Series[Any] The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. @@ -232,794 +232,794 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Given two nodes, calculate Total Neighbors -.. py:function:: gds.alpha.maxkcut.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.maxkcut.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Approximate Maximum k-cut maps each node into one of k disjoint communities trying to maximize the sum of weights of relationships between these communities. -.. py:function:: gds.alpha.maxkcut.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.maxkcut.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Approximate Maximum k-cut maps each node into one of k disjoint communities trying to maximize the sum of weights of relationships between these communities. -.. py:function:: gds.alpha.maxkcut.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.maxkcut.stream(G: Graph, **config: Any) -> pandas.DataFrame Approximate Maximum k-cut maps each node into one of k disjoint communities trying to maximize the sum of weights of relationships between these communities. -.. py:function:: gds.alpha.maxkcut.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.maxkcut.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Approximate Maximum k-cut maps each node into one of k disjoint communities trying to maximize the sum of weights of relationships between these communities. -.. py:function:: gds.alpha.modularity.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.modularity.stats(G: Graph, **config: Any) -> pandas.Series[Any] -.. py:function:: gds.alpha.modularity.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.modularity.stream(G: Graph, **config: Any) -> pandas.DataFrame -.. py:function:: gds.alpha.nodeSimilarity.filtered.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.nodeSimilarity.filtered.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. The algorithm computes pair-wise similarities based on Jaccard or Overlap metrics. The filtered variant supports limiting which nodes to compare via source and target node filters. -.. py:function:: gds.alpha.nodeSimilarity.filtered.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.nodeSimilarity.filtered.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.alpha.nodeSimilarity.filtered.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.nodeSimilarity.filtered.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. The algorithm computes pair-wise similarities based on Jaccard or Overlap metrics. The filtered variant supports limiting which nodes to compare via source and target node filters. -.. py:function:: gds.alpha.nodeSimilarity.filtered.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.nodeSimilarity.filtered.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.alpha.nodeSimilarity.filtered.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.nodeSimilarity.filtered.stream(G: Graph, **config: Any) -> pandas.DataFrame The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. The algorithm computes pair-wise similarities based on Jaccard or Overlap metrics. The filtered variant supports limiting which nodes to compare via source and target node filters. -.. py:function:: gds.alpha.nodeSimilarity.filtered.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.nodeSimilarity.filtered.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.alpha.nodeSimilarity.filtered.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.nodeSimilarity.filtered.write(G: Graph, **config: Any) -> pandas.Series[Any] The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. The algorithm computes pair-wise similarities based on Jaccard or Overlap metrics. The filtered variant supports limiting which nodes to compare via source and target node filters. -.. py:function:: gds.alpha.nodeSimilarity.filtered.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.nodeSimilarity.filtered.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.alpha.scaleProperties.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.scaleProperties.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Scale node properties .. deprecated:: 2.4.0 Since GDS server version 2.4.0 you should use the endpoint :func:`gds.scaleProperties.mutate` instead. -.. py:function:: gds.alpha.scaleProperties.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.scaleProperties.stream(G: Graph, **config: Any) -> pandas.DataFrame Scale node properties .. deprecated:: 2.4.0 Since GDS server version 2.4.0 you should use the endpoint :func:`gds.scaleProperties.stream` instead. -.. py:function:: gds.alpha.scc.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.scc.stream(G: Graph, **config: Any) -> pandas.DataFrame The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component. -.. py:function:: gds.alpha.scc.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.scc.write(G: Graph, **config: Any) -> pandas.Series[Any] The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component. -.. py:function:: gds.alpha.sllpa.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.sllpa.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.sllpa.mutate` instead. -.. py:function:: gds.alpha.sllpa.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.sllpa.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.sllpa.mutate.estimate` instead. -.. py:function:: gds.alpha.sllpa.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.sllpa.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.sllpa.stats` instead. -.. py:function:: gds.alpha.sllpa.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.sllpa.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.sllpa.stats.estimate` instead. -.. py:function:: gds.alpha.sllpa.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.sllpa.stream(G: Graph, **config: Any) -> pandas.DataFrame The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.sllpa.stream` instead. -.. py:function:: gds.alpha.sllpa.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.sllpa.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.sllpa.stream.estimate` instead. -.. py:function:: gds.alpha.sllpa.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.sllpa.write(G: Graph, **config: Any) -> pandas.Series[Any] The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.sllpa.write` instead. -.. py:function:: gds.alpha.sllpa.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.sllpa.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.sllpa.write.estimate` instead. -.. py:function:: gds.alpha.triangles(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.alpha.triangles(G: Graph, **config: Any) -> pandas.DataFrame Triangles streams the nodeIds of each triangle in the graph. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.triangles` instead. -.. py:function:: gds.articleRank.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articleRank.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Article Rank is a variant of the Page Rank algorithm, which measures the transitive influence or connectivity of nodes. -.. py:function:: gds.articleRank.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articleRank.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.articleRank.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articleRank.stats(G: Graph, **config: Any) -> pandas.Series[Any] Executes the algorithm and returns result statistics without writing the result to Neo4j. -.. py:function:: gds.articleRank.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articleRank.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.articleRank.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.articleRank.stream(G: Graph, **config: Any) -> pandas.DataFrame Article Rank is a variant of the Page Rank algorithm, which measures the transitive influence or connectivity of nodes. -.. py:function:: gds.articleRank.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articleRank.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.articleRank.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articleRank.write(G: Graph, **config: Any) -> pandas.Series[Any] Article Rank is a variant of the Page Rank algorithm, which measures the transitive influence or connectivity of nodes. -.. py:function:: gds.articleRank.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articleRank.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.articulationPoints.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articulationPoints.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Articulation Points is an algorithm that finds nodes that disconnect components if removed. -.. py:function:: gds.articulationPoints.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articulationPoints.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.articulationPoints.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articulationPoints.stats(G: Graph, **config: Any) -> pandas.Series[Any] Articulation Points is an algorithm that finds nodes that disconnect components if removed. -.. py:function:: gds.articulationPoints.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articulationPoints.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.articulationPoints.stream(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articulationPoints.stream(G: Graph, **config: Any) -> pandas.Series[Any] Articulation Points is an algorithm that finds nodes that disconnect components if removed. -.. py:function:: gds.articulationPoints.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articulationPoints.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.articulationPoints.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articulationPoints.write(G: Graph, **config: Any) -> pandas.Series[Any] Articulation Points is an algorithm that finds nodes that disconnect components if removed. -.. py:function:: gds.articulationPoints.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.articulationPoints.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.bellmanFord.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bellmanFord.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Bellman-Ford shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph without negative cycles. -.. py:function:: gds.bellmanFord.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bellmanFord.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.bellmanFord.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bellmanFord.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Bellman-Ford shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph without negative cycles. -.. py:function:: gds.bellmanFord.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bellmanFord.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.bellmanFord.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.bellmanFord.stream(G: Graph, **config: Any) -> pandas.DataFrame The Bellman-Ford shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph without negative cycles. -.. py:function:: gds.bellmanFord.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bellmanFord.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.bellmanFord.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bellmanFord.write(G: Graph, **config: Any) -> pandas.Series[Any] The Bellman-Ford shortest path algorithm computes the shortest (weighted) path between one node and any other node in the graph without negative cycles. -.. py:function:: gds.bellmanFord.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bellmanFord.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.closeness.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.closeness.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.beta.closeness.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.closeness.stats(G: Graph, **config: Any) -> pandas.Series[Any] Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.beta.closeness.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.closeness.stream(G: Graph, **config: Any) -> pandas.DataFrame Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.beta.closeness.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.closeness.write(G: Graph, **config: Any) -> pandas.Series[Any] Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.beta.collapsePath.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.collapsePath.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Collapse Path algorithm is a traversal algorithm capable of creating relationships between the start and end nodes of a traversal -.. py:function:: gds.beta.influenceMaximization.celf.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.influenceMaximization.celf.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes that maximize the expected spread of influence in the network. -.. py:function:: gds.beta.influenceMaximization.celf.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.influenceMaximization.celf.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.influenceMaximization.celf.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.influenceMaximization.celf.stats(G: Graph, **config: Any) -> pandas.Series[Any] Executes the algorithm and returns result statistics without writing the result to Neo4j. -.. py:function:: gds.beta.influenceMaximization.celf.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.influenceMaximization.celf.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.influenceMaximization.celf.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.influenceMaximization.celf.stream(G: Graph, **config: Any) -> pandas.DataFrame The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes that maximize the expected spread of influence in the network. -.. py:function:: gds.beta.influenceMaximization.celf.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.influenceMaximization.celf.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes that maximize the expected spread of influence in the network. -.. py:function:: gds.beta.influenceMaximization.celf.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.influenceMaximization.celf.write(G: Graph, **config: Any) -> pandas.Series[Any] The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes that maximize the expected spread of influence in the network. -.. py:function:: gds.beta.influenceMaximization.celf.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.influenceMaximization.celf.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.k1coloring.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.k1coloring.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The K-1 Coloring algorithm assigns a color to every node in the graph. -.. py:function:: gds.beta.k1coloring.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.k1coloring.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.k1coloring.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.k1coloring.stats(G: Graph, **config: Any) -> pandas.Series[Any] The K-1 Coloring algorithm assigns a color to every node in the graph. -.. py:function:: gds.beta.k1coloring.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.k1coloring.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.k1coloring.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.k1coloring.stream(G: Graph, **config: Any) -> pandas.DataFrame The K-1 Coloring algorithm assigns a color to every node in the graph. -.. py:function:: gds.beta.k1coloring.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.k1coloring.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.k1coloring.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.k1coloring.write(G: Graph, **config: Any) -> pandas.Series[Any] The K-1 Coloring algorithm assigns a color to every node in the graph. -.. py:function:: gds.beta.k1coloring.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.k1coloring.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.kmeans.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.kmeans.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Kmeans algorithm clusters nodes into different communities based on Euclidean distance .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.kmeans.mutate` instead. -.. py:function:: gds.beta.kmeans.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.kmeans.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.kmeans.mutate.estimate` instead. -.. py:function:: gds.beta.kmeans.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.kmeans.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Kmeans algorithm clusters nodes into different communities based on Euclidean distance .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.kmeans.stats` instead. -.. py:function:: gds.beta.kmeans.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.kmeans.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.kmeans.stats.estimate` instead. -.. py:function:: gds.beta.kmeans.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.kmeans.stream(G: Graph, **config: Any) -> pandas.DataFrame The Kmeans algorithm clusters nodes into different communities based on Euclidean distance .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.kmeans.stream` instead. -.. py:function:: gds.beta.kmeans.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.kmeans.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.kmeans.stream.estimate` instead. -.. py:function:: gds.beta.kmeans.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.kmeans.write(G: Graph, **config: Any) -> pandas.Series[Any] The Kmeans algorithm clusters nodes into different communities based on Euclidean distance .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.kmeans.write` instead. -.. py:function:: gds.beta.kmeans.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.kmeans.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.kmeans.write.estimate` instead. -.. py:function:: gds.beta.leiden.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.leiden.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Leiden is a community detection algorithm, which guarantees that communities are well connected -.. py:function:: gds.beta.leiden.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.leiden.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.leiden.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.leiden.stats(G: Graph, **config: Any) -> pandas.Series[Any] Executes the algorithm and returns result statistics without writing the result to Neo4j. -.. py:function:: gds.beta.leiden.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.leiden.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.leiden.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.leiden.stream(G: Graph, **config: Any) -> pandas.DataFrame Leiden is a community detection algorithm, which guarantees that communities are well connected -.. py:function:: gds.beta.leiden.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.leiden.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.leiden.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.leiden.write(G: Graph, **config: Any) -> pandas.Series[Any] Leiden is a community detection algorithm, which guarantees that communities are well connected -.. py:function:: gds.beta.leiden.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.leiden.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.modularityOptimization.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.modularityOptimization.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity. -.. py:function:: gds.beta.modularityOptimization.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.modularityOptimization.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.modularityOptimization.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.modularityOptimization.stream(G: Graph, **config: Any) -> pandas.DataFrame The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity. -.. py:function:: gds.beta.modularityOptimization.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.modularityOptimization.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.modularityOptimization.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.modularityOptimization.write(G: Graph, **config: Any) -> pandas.Series[Any] The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity. -.. py:function:: gds.beta.modularityOptimization.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.modularityOptimization.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.spanningTree.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.spanningTree.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The spanning tree algorithm visits all nodes that are in the same connected component as the starting node, and returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized. -.. py:function:: gds.beta.spanningTree.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.spanningTree.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.spanningTree.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.spanningTree.stats(G: Graph, **config: Any) -> pandas.Series[Any] The spanning tree algorithm visits all nodes that are in the same connected component as the starting node, and returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized. -.. py:function:: gds.beta.spanningTree.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.spanningTree.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.spanningTree.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.spanningTree.stream(G: Graph, **config: Any) -> pandas.DataFrame The spanning tree algorithm visits all nodes that are in the same connected component as the starting node, and returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized. -.. py:function:: gds.beta.spanningTree.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.spanningTree.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.spanningTree.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.spanningTree.write(G: Graph, **config: Any) -> pandas.Series[Any] The spanning tree algorithm visits all nodes that are in the same connected component as the starting node, and returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized. -.. py:function:: gds.beta.spanningTree.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.spanningTree.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.steinerTree.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.steinerTree.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The steiner tree algorithm accepts a source node, as well as a list of target nodes. It then attempts to find a spanning tree where there is a path from the source node to each target node, such that the total weight of the relationships is as low as possible. -.. py:function:: gds.beta.steinerTree.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.steinerTree.stats(G: Graph, **config: Any) -> pandas.Series[Any] The steiner tree algorithm accepts a source node, as well as a list of target nodes. It then attempts to find a spanning tree where there is a path from the source node to each target node, such that the total weight of the relationships is as low as possible. -.. py:function:: gds.beta.steinerTree.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.steinerTree.stream(G: Graph, **config: Any) -> pandas.DataFrame The steiner tree algorithm accepts a source node, as well as a list of target nodes. It then attempts to find a spanning tree where there is a path from the source node to each target node, such that the total weight of the relationships is as low as possible. -.. py:function:: gds.beta.steinerTree.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.steinerTree.write(G: Graph, **config: Any) -> pandas.Series[Any] The steiner tree algorithm accepts a source node, as well as a list of target nodes. It then attempts to find a spanning tree where there is a path from the source node to each target node, such that the total weight of the relationships is as low as possible. -.. py:function:: gds.betweenness.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.betweenness.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Betweenness centrality measures the relative information flow that passes through a node. -.. py:function:: gds.betweenness.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.betweenness.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Betweenness centrality measures the relative information flow that passes through a node. -.. py:function:: gds.betweenness.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.betweenness.stats(G: Graph, **config: Any) -> pandas.Series[Any] Betweenness centrality measures the relative information flow that passes through a node. -.. py:function:: gds.betweenness.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.betweenness.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Betweenness centrality measures the relative information flow that passes through a node. -.. py:function:: gds.betweenness.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.betweenness.stream(G: Graph, **config: Any) -> pandas.DataFrame Betweenness centrality measures the relative information flow that passes through a node. -.. py:function:: gds.betweenness.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.betweenness.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Betweenness centrality measures the relative information flow that passes through a node. -.. py:function:: gds.betweenness.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.betweenness.write(G: Graph, **config: Any) -> pandas.Series[Any] Betweenness centrality measures the relative information flow that passes through a node. -.. py:function:: gds.betweenness.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.betweenness.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Betweenness centrality measures the relative information flow that passes through a node. -.. py:function:: gds.bfs.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bfs.mutate(G: Graph, **config: Any) -> pandas.Series[Any] BFS is a traversal algorithm, which explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level. -.. py:function:: gds.bfs.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bfs.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.bfs.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bfs.stats(G: Graph, **config: Any) -> pandas.Series[Any] BFS is a traversal algorithm, which explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level. -.. py:function:: gds.bfs.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bfs.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.bfs.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.bfs.stream(G: Graph, **config: Any) -> pandas.DataFrame BFS is a traversal algorithm, which explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level. -.. py:function:: gds.bfs.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bfs.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] BFS is a traversal algorithm, which explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level. -.. py:function:: gds.bridges.stream(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bridges.stream(G: Graph, **config: Any) -> pandas.Series[Any] An algorithm to find Bridge edges in a graph. -.. py:function:: gds.bridges.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.bridges.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.cliqueCounting.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.cliqueCounting.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Counts the number of cliques, of different size, each node is part of. -.. py:function:: gds.cliqueCounting.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.cliqueCounting.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.cliqueCounting.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.cliqueCounting.stats(G: Graph, **config: Any) -> pandas.Series[Any] Counts the number of cliques, of different size, in the entire graph. -.. py:function:: gds.cliqueCounting.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.cliqueCounting.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.cliqueCounting.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.cliqueCounting.stream(G: Graph, **config: Any) -> pandas.DataFrame Counts the number of cliques, of different size, each node is part of. -.. py:function:: gds.cliqueCounting.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.cliqueCounting.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.cliqueCounting.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.cliqueCounting.write(G: Graph, **config: Any) -> pandas.Series[Any] Counts the number of cliques, of different size, each node is part of. -.. py:function:: gds.cliqueCounting.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.cliqueCounting.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.closeness.harmonic.mutate(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.closeness.harmonic.mutate(G: Graph, **config: Any) -> pandas.DataFrame Harmonic centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.closeness.harmonic.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.harmonic.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.closeness.harmonic.stats(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.closeness.harmonic.stats(G: Graph, **config: Any) -> pandas.DataFrame Harmonic centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.closeness.harmonic.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.harmonic.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.closeness.harmonic.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.closeness.harmonic.stream(G: Graph, **config: Any) -> pandas.DataFrame Harmonic centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.closeness.harmonic.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.harmonic.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.closeness.harmonic.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.harmonic.write(G: Graph, **config: Any) -> pandas.Series[Any] Harmonic centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.closeness.harmonic.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.harmonic.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.closeness.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.closeness.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.closeness.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.stats(G: Graph, **config: Any) -> pandas.Series[Any] Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.closeness.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.closeness.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.closeness.stream(G: Graph, **config: Any) -> pandas.DataFrame Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.closeness.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.closeness.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.write(G: Graph, **config: Any) -> pandas.Series[Any] Closeness centrality is a way of detecting nodes that are able to spread information very efficiently through a graph. -.. py:function:: gds.closeness.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.closeness.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.collapsePath.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.collapsePath.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Collapse Path algorithm is a traversal algorithm capable of creating relationships between the start and end nodes of a traversal -.. py:function:: gds.conductance.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.conductance.stream(G: Graph, **config: Any) -> pandas.DataFrame Evaluates a division of nodes into communities based on the proportion of relationships that cross community boundaries. -.. py:function:: gds.dag.longestPath.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.dag.longestPath.stream(G: Graph, **config: Any) -> pandas.DataFrame Finds the longest path that leads to a node in a directed acyclic graph (DAG). -.. py:function:: gds.dag.topologicalSort.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.dag.topologicalSort.stream(G: Graph, **config: Any) -> pandas.DataFrame Returns a topological ordering of the nodes in a directed acyclic graph (DAG). -.. py:function:: gds.degree.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.degree.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Degree centrality measures the number of incoming and outgoing relationships from a node. -.. py:function:: gds.degree.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.degree.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Degree centrality measures the number of incoming and outgoing relationships from a node. -.. py:function:: gds.degree.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.degree.stats(G: Graph, **config: Any) -> pandas.Series[Any] Degree centrality measures the number of incoming and outgoing relationships from a node. -.. py:function:: gds.degree.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.degree.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Degree centrality measures the number of incoming and outgoing relationships from a node. -.. py:function:: gds.degree.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.degree.stream(G: Graph, **config: Any) -> pandas.DataFrame Degree centrality measures the number of incoming and outgoing relationships from a node. -.. py:function:: gds.degree.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.degree.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Degree centrality measures the number of incoming and outgoing relationships from a node. -.. py:function:: gds.degree.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.degree.write(G: Graph, **config: Any) -> pandas.Series[Any] Degree centrality measures the number of incoming and outgoing relationships from a node. -.. py:function:: gds.degree.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.degree.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Degree centrality measures the number of incoming and outgoing relationships from a node. -.. py:function:: gds.dfs.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.dfs.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking. -.. py:function:: gds.dfs.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.dfs.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.dfs.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.dfs.stream(G: Graph, **config: Any) -> pandas.DataFrame Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking. -.. py:function:: gds.dfs.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.dfs.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking. -.. py:function:: gds.eigenvector.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.eigenvector.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Eigenvector Centrality is an algorithm that measures the transitive influence or connectivity of nodes. -.. py:function:: gds.eigenvector.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.eigenvector.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.eigenvector.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.eigenvector.stats(G: Graph, **config: Any) -> pandas.Series[Any] Eigenvector Centrality is an algorithm that measures the transitive influence or connectivity of nodes. -.. py:function:: gds.eigenvector.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.eigenvector.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.eigenvector.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.eigenvector.stream(G: Graph, **config: Any) -> pandas.DataFrame Eigenvector Centrality is an algorithm that measures the transitive influence or connectivity of nodes. -.. py:function:: gds.eigenvector.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.eigenvector.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.eigenvector.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.eigenvector.write(G: Graph, **config: Any) -> pandas.Series[Any] Eigenvector Centrality is an algorithm that measures the transitive influence or connectivity of nodes. -.. py:function:: gds.eigenvector.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.eigenvector.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. @@ -1027,7 +1027,7 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Constructs a random subgraph based on common-neighbour-aware random walks. -.. py:function:: gds.graph.sample.cnarw.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.graph.sample.cnarw.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. @@ -1035,351 +1035,351 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Constructs a random subgraph based on random walks with restarts. -.. py:function:: gds.hdbscan.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hdbscan.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Hierarchical Density-Based Spatial Clustering of Applications with Noise -.. py:function:: gds.hdbscan.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hdbscan.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.hdbscan.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hdbscan.stats(G: Graph, **config: Any) -> pandas.Series[Any] Hierarchical Density-Based Spatial Clustering of Applications with Noise -.. py:function:: gds.hdbscan.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hdbscan.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.hdbscan.stream(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hdbscan.stream(G: Graph, **config: Any) -> pandas.Series[Any] Hierarchical Density-Based Spatial Clustering of Applications with Noise -.. py:function:: gds.hdbscan.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hdbscan.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.hdbscan.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hdbscan.write(G: Graph, **config: Any) -> pandas.Series[Any] Hierarchical Density-Based Spatial Clustering of Applications with Noise -.. py:function:: gds.hdbscan.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hdbscan.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.hits.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hits.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes. -.. py:function:: gds.hits.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hits.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.hits.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hits.stats(G: Graph, **config: Any) -> pandas.Series[Any] Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes. -.. py:function:: gds.hits.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hits.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.hits.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.hits.stream(G: Graph, **config: Any) -> pandas.DataFrame Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes. -.. py:function:: gds.hits.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hits.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.hits.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hits.write(G: Graph, **config: Any) -> pandas.Series[Any] Hyperlink-Induced Topic Search (HITS) is a link analysis algorithm that rates nodes. -.. py:function:: gds.hits.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hits.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.influenceMaximization.celf.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.influenceMaximization.celf.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes that maximize the expected spread of influence in the network. -.. py:function:: gds.influenceMaximization.celf.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.influenceMaximization.celf.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.influenceMaximization.celf.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.influenceMaximization.celf.stats(G: Graph, **config: Any) -> pandas.Series[Any] Executes the algorithm and returns result statistics without writing the result to Neo4j. -.. py:function:: gds.influenceMaximization.celf.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.influenceMaximization.celf.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.influenceMaximization.celf.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.influenceMaximization.celf.stream(G: Graph, **config: Any) -> pandas.DataFrame The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes that maximize the expected spread of influence in the network. -.. py:function:: gds.influenceMaximization.celf.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.influenceMaximization.celf.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes that maximize the expected spread of influence in the network. -.. py:function:: gds.influenceMaximization.celf.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.influenceMaximization.celf.write(G: Graph, **config: Any) -> pandas.Series[Any] The Cost Effective Lazy Forward (CELF) algorithm aims to find k nodes that maximize the expected spread of influence in the network. -.. py:function:: gds.influenceMaximization.celf.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.influenceMaximization.celf.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.k1coloring.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.k1coloring.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The K-1 Coloring algorithm assigns a color to every node in the graph. -.. py:function:: gds.k1coloring.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.k1coloring.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.k1coloring.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.k1coloring.stats(G: Graph, **config: Any) -> pandas.Series[Any] The K-1 Coloring algorithm assigns a color to every node in the graph. -.. py:function:: gds.k1coloring.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.k1coloring.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.k1coloring.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.k1coloring.stream(G: Graph, **config: Any) -> pandas.DataFrame The K-1 Coloring algorithm assigns a color to every node in the graph. -.. py:function:: gds.k1coloring.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.k1coloring.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.k1coloring.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.k1coloring.write(G: Graph, **config: Any) -> pandas.Series[Any] The K-1 Coloring algorithm assigns a color to every node in the graph. -.. py:function:: gds.k1coloring.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.k1coloring.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.kSpanningTree.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kSpanningTree.write(G: Graph, **config: Any) -> pandas.Series[Any] The K-spanning tree algorithm starts from a root node and returns a spanning tree with exactly k nodes -.. py:function:: gds.kcore.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kcore.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Computes the k-core values in a network -.. py:function:: gds.kcore.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kcore.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.kcore.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kcore.stats(G: Graph, **config: Any) -> pandas.Series[Any] Computes the k-core values in a network -.. py:function:: gds.kcore.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kcore.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.kcore.stream(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kcore.stream(G: Graph, **config: Any) -> pandas.Series[Any] Computes the k-core values in a network -.. py:function:: gds.kcore.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kcore.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.kcore.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kcore.write(G: Graph, **config: Any) -> pandas.Series[Any] Computes the k-core values in a network -.. py:function:: gds.kcore.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kcore.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.kmeans.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kmeans.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Kmeans algorithm clusters nodes into different communities based on Euclidean distance -.. py:function:: gds.kmeans.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kmeans.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.kmeans.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kmeans.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Kmeans algorithm clusters nodes into different communities based on Euclidean distance -.. py:function:: gds.kmeans.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kmeans.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.kmeans.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.kmeans.stream(G: Graph, **config: Any) -> pandas.DataFrame The Kmeans algorithm clusters nodes into different communities based on Euclidean distance -.. py:function:: gds.kmeans.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kmeans.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.kmeans.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kmeans.write(G: Graph, **config: Any) -> pandas.Series[Any] The Kmeans algorithm clusters nodes into different communities based on Euclidean distance -.. py:function:: gds.kmeans.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.kmeans.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.knn.filtered.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.filtered.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties. Filtered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively. - .. py:function:: gds.knn.filtered.mutate.estimate(G: Graph, **config: Any) -> Series[Any] + .. py:function:: gds.knn.filtered.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.knn.filtered.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.filtered.stats(G: Graph, **config: Any) -> pandas.Series[Any] The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties. Filtered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively. -.. py:function:: gds.knn.filtered.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.filtered.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.knn.filtered.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.knn.filtered.stream(G: Graph, **config: Any) -> pandas.DataFrame The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties. Filtered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively. -.. py:function:: gds.knn.filtered.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.filtered.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.knn.filtered.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.filtered.write(G: Graph, **config: Any) -> pandas.Series[Any] The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties. Filtered KNN extends this functionality, allowing filtering on source nodes and target nodes, respectively. -.. py:function:: gds.knn.filtered.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.filtered.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.knn.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties -.. py:function:: gds.knn.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.knn.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.stats(G: Graph, **config: Any) -> pandas.Series[Any] The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties -.. py:function:: gds.knn.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.knn.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.knn.stream(G: Graph, **config: Any) -> pandas.DataFrame The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties -.. py:function:: gds.knn.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.knn.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.write(G: Graph, **config: Any) -> pandas.Series[Any] The k-nearest neighbor graph algorithm constructs relationships between nodes if the distance between two nodes is among the k nearest distances compared to other nodes. KNN computes distances based on the similarity of node properties -.. py:function:: gds.knn.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.knn.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.labelPropagation.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.labelPropagation.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Label Propagation algorithm is a fast algorithm for finding communities in a graph. -.. py:function:: gds.labelPropagation.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.labelPropagation.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.labelPropagation.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.labelPropagation.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Label Propagation algorithm is a fast algorithm for finding communities in a graph. -.. py:function:: gds.labelPropagation.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.labelPropagation.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.labelPropagation.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.labelPropagation.stream(G: Graph, **config: Any) -> pandas.DataFrame The Label Propagation algorithm is a fast algorithm for finding communities in a graph. -.. py:function:: gds.labelPropagation.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.labelPropagation.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.labelPropagation.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.labelPropagation.write(G: Graph, **config: Any) -> pandas.Series[Any] The Label Propagation algorithm is a fast algorithm for finding communities in a graph. -.. py:function:: gds.labelPropagation.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.labelPropagation.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.leiden.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.leiden.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Leiden is a community detection algorithm, which guarantees that communities are well connected -.. py:function:: gds.leiden.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.leiden.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.leiden.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.leiden.stats(G: Graph, **config: Any) -> pandas.Series[Any] Executes the algorithm and returns result statistics without writing the result to Neo4j. -.. py:function:: gds.leiden.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.leiden.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.leiden.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.leiden.stream(G: Graph, **config: Any) -> pandas.DataFrame Leiden is a community detection algorithm, which guarantees that communities are well connected -.. py:function:: gds.leiden.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.leiden.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.leiden.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.leiden.write(G: Graph, **config: Any) -> pandas.Series[Any] Leiden is a community detection algorithm, which guarantees that communities are well connected -.. py:function:: gds.leiden.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.leiden.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. @@ -1387,660 +1387,660 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Given two nodes, calculate Adamic Adar similarity -.. py:function:: gds.localClusteringCoefficient.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.localClusteringCoefficient.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The local clustering coefficient is a metric quantifying how connected the neighborhood of a node is. -.. py:function:: gds.localClusteringCoefficient.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.localClusteringCoefficient.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.localClusteringCoefficient.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.localClusteringCoefficient.stats(G: Graph, **config: Any) -> pandas.Series[Any] Executes the algorithm and returns result statistics without writing the result to Neo4j. -.. py:function:: gds.localClusteringCoefficient.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.localClusteringCoefficient.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.localClusteringCoefficient.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.localClusteringCoefficient.stream(G: Graph, **config: Any) -> pandas.DataFrame The local clustering coefficient is a metric quantifying how connected the neighborhood of a node is. -.. py:function:: gds.localClusteringCoefficient.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.localClusteringCoefficient.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.localClusteringCoefficient.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.localClusteringCoefficient.write(G: Graph, **config: Any) -> pandas.Series[Any] The local clustering coefficient is a metric quantifying how connected the neighborhood of a node is. -.. py:function:: gds.localClusteringCoefficient.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.localClusteringCoefficient.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.louvain.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.louvain.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Louvain method for community detection is an algorithm for detecting communities in networks. -.. py:function:: gds.louvain.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.louvain.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.louvain.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.louvain.stats(G: Graph, **config: Any) -> pandas.Series[Any] Executes the algorithm and returns result statistics without writing the result to Neo4j. -.. py:function:: gds.louvain.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.louvain.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.louvain.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.louvain.stream(G: Graph, **config: Any) -> pandas.DataFrame The Louvain method for community detection is an algorithm for detecting communities in networks. -.. py:function:: gds.louvain.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.louvain.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.louvain.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.louvain.write(G: Graph, **config: Any) -> pandas.Series[Any] The Louvain method for community detection is an algorithm for detecting communities in networks. -.. py:function:: gds.louvain.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.louvain.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.maxFlow.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxFlow.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Computes the maximum flow between two nodes or sets of nodes in a graph. -.. py:function:: gds.maxFlow.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxFlow.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.maxFlow.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxFlow.stats(G: Graph, **config: Any) -> pandas.Series[Any] Computes the maximum flow between two nodes or sets of nodes in a graph. -.. py:function:: gds.maxFlow.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxFlow.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.maxFlow.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.maxFlow.stream(G: Graph, **config: Any) -> pandas.DataFrame Computes the maximum flow between two nodes or sets of nodes in a graph. -.. py:function:: gds.maxFlow.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxFlow.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.maxFlow.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxFlow.write(G: Graph, **config: Any) -> pandas.Series[Any] Computes the maximum flow between two nodes or sets of nodes in a graph. -.. py:function:: gds.maxFlow.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxFlow.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.maxkcut.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxkcut.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Approximate Maximum k-cut maps each node into one of k disjoint communities trying to maximize the sum of weights of relationships between these communities. -.. py:function:: gds.maxkcut.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxkcut.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Approximate Maximum k-cut maps each node into one of k disjoint communities trying to maximize the sum of weights of relationships between these communities. -.. py:function:: gds.maxkcut.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.maxkcut.stream(G: Graph, **config: Any) -> pandas.DataFrame Approximate Maximum k-cut maps each node into one of k disjoint communities trying to maximize the sum of weights of relationships between these communities. -.. py:function:: gds.maxkcut.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.maxkcut.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Approximate Maximum k-cut maps each node into one of k disjoint communities trying to maximize the sum of weights of relationships between these communities. -.. py:function:: gds.modularity.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularity.stats(G: Graph, **config: Any) -> pandas.Series[Any] -.. py:function:: gds.modularity.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularity.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] -.. py:function:: gds.modularity.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.modularity.stream(G: Graph, **config: Any) -> pandas.DataFrame -.. py:function:: gds.modularity.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularity.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] -.. py:function:: gds.modularityOptimization.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularityOptimization.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity. -.. py:function:: gds.modularityOptimization.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularityOptimization.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.modularityOptimization.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularityOptimization.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity. -.. py:function:: gds.modularityOptimization.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularityOptimization.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.modularityOptimization.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.modularityOptimization.stream(G: Graph, **config: Any) -> pandas.DataFrame The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity. -.. py:function:: gds.modularityOptimization.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularityOptimization.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.modularityOptimization.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularityOptimization.write(G: Graph, **config: Any) -> pandas.Series[Any] The Modularity Optimization algorithm groups the nodes in the graph by optimizing the graphs modularity. -.. py:function:: gds.modularityOptimization.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.modularityOptimization.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.nodeSimilarity.filtered.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.filtered.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. The algorithm computes pair-wise similarities based on Jaccard or Overlap metrics. The filtered variant supports limiting which nodes to compare via source and target node filters. -.. py:function:: gds.nodeSimilarity.filtered.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.filtered.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.nodeSimilarity.filtered.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.filtered.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. The algorithm computes pair-wise similarities based on Jaccard or Overlap metrics. The filtered variant supports limiting which nodes to compare via source and target node filters. -.. py:function:: gds.nodeSimilarity.filtered.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.filtered.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.nodeSimilarity.filtered.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.nodeSimilarity.filtered.stream(G: Graph, **config: Any) -> pandas.DataFrame The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. The algorithm computes pair-wise similarities based on Jaccard or Overlap metrics. The filtered variant supports limiting which nodes to compare via source and target node filters. -.. py:function:: gds.nodeSimilarity.filtered.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.filtered.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.nodeSimilarity.filtered.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.filtered.write(G: Graph, **config: Any) -> pandas.Series[Any] The Filtered Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. The algorithm computes pair-wise similarities based on Jaccard or Overlap metrics. The filtered variant supports limiting which nodes to compare via source and target node filters. -.. py:function:: gds.nodeSimilarity.filtered.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.filtered.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.nodeSimilarity.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. Node Similarity computes pair-wise similarities based on the Jaccard metric. -.. py:function:: gds.nodeSimilarity.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.nodeSimilarity.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. Node Similarity computes pair-wise similarities based on the Jaccard metric. -.. py:function:: gds.nodeSimilarity.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.nodeSimilarity.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.nodeSimilarity.stream(G: Graph, **config: Any) -> pandas.DataFrame The Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. Node Similarity computes pair-wise similarities based on the Jaccard metric. -.. py:function:: gds.nodeSimilarity.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.nodeSimilarity.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.write(G: Graph, **config: Any) -> pandas.Series[Any] The Node Similarity algorithm compares a set of nodes based on the nodes they are connected to. Two nodes are considered similar if they share many of the same neighbors. Node Similarity computes pair-wise similarities based on the Jaccard metric. -.. py:function:: gds.nodeSimilarity.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.nodeSimilarity.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.pageRank.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.pageRank.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Page Rank is an algorithm that measures the transitive influence or connectivity of nodes. -.. py:function:: gds.pageRank.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.pageRank.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.pageRank.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.pageRank.stats(G: Graph, **config: Any) -> pandas.Series[Any] Executes the algorithm and returns result statistics without writing the result to Neo4j. -.. py:function:: gds.pageRank.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.pageRank.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.pageRank.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.pageRank.stream(G: Graph, **config: Any) -> pandas.DataFrame Page Rank is an algorithm that measures the transitive influence or connectivity of nodes. -.. py:function:: gds.pageRank.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.pageRank.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.pageRank.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.pageRank.write(G: Graph, **config: Any) -> pandas.Series[Any] Page Rank is an algorithm that measures the transitive influence or connectivity of nodes. -.. py:function:: gds.pageRank.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.pageRank.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.prizeSteinerTree.mutate(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.prizeSteinerTree.mutate(G: Graph, **config: Any) -> pandas.DataFrame An approximation algorithm for the prize collector steiner tree problem -.. py:function:: gds.prizeSteinerTree.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.prizeSteinerTree.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.prizeSteinerTree.stats(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.prizeSteinerTree.stats(G: Graph, **config: Any) -> pandas.DataFrame An approximation algorithm for the prize collector steiner tree problem -.. py:function:: gds.prizeSteinerTree.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.prizeSteinerTree.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.prizeSteinerTree.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.prizeSteinerTree.stream(G: Graph, **config: Any) -> pandas.DataFrame An approximation algorithm for the prize collector steiner tree problem -.. py:function:: gds.prizeSteinerTree.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.prizeSteinerTree.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.prizeSteinerTree.write(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.prizeSteinerTree.write(G: Graph, **config: Any) -> pandas.DataFrame An approximation algorithm for the prize collector steiner tree problem -.. py:function:: gds.prizeSteinerTree.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.prizeSteinerTree.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.randomWalk.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.randomWalk.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Random Walk is an algorithm that provides random paths in a graph. It’s similar to how a drunk person traverses a city. The mutate procedure produces the count a node occurs on a random walk. -.. py:function:: gds.randomWalk.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.randomWalk.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.randomWalk.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.randomWalk.stats(G: Graph, **config: Any) -> pandas.Series[Any] Random Walk is an algorithm that provides random paths in a graph. It’s similar to how a drunk person traverses a city. -.. py:function:: gds.randomWalk.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.randomWalk.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.randomWalk.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.randomWalk.stream(G: Graph, **config: Any) -> pandas.DataFrame Random Walk is an algorithm that provides random paths in a graph. It’s similar to how a drunk person traverses a city. -.. py:function:: gds.randomWalk.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.randomWalk.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.scaleProperties.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scaleProperties.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Scale node properties -.. py:function:: gds.scaleProperties.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scaleProperties.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.scaleProperties.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scaleProperties.stats(G: Graph, **config: Any) -> pandas.Series[Any] Scale node properties -.. py:function:: gds.scaleProperties.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scaleProperties.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.scaleProperties.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.scaleProperties.stream(G: Graph, **config: Any) -> pandas.DataFrame Scale node properties -.. py:function:: gds.scaleProperties.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scaleProperties.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.scaleProperties.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scaleProperties.write(G: Graph, **config: Any) -> pandas.Series[Any] Scale node properties -.. py:function:: gds.scaleProperties.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scaleProperties.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.scc.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scc.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component. -.. py:function:: gds.scc.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scc.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for SCC. -.. py:function:: gds.scc.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scc.stats(G: Graph, **config: Any) -> pandas.Series[Any] The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component. -.. py:function:: gds.scc.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scc.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for SCC. -.. py:function:: gds.scc.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.scc.stream(G: Graph, **config: Any) -> pandas.DataFrame The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component. -.. py:function:: gds.scc.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scc.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for SCC. -.. py:function:: gds.scc.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scc.write(G: Graph, **config: Any) -> pandas.Series[Any] The SCC algorithm finds sets of connected nodes in an directed graph, where all nodes in the same set form a connected component. -.. py:function:: gds.scc.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.scc.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for SCC. -.. py:function:: gds.shortestPath.astar.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.astar.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The A* shortest path algorithm computes the shortest path between a pair of nodes. It uses the relationship weight property to compare path lengths. In addition, this implementation uses the haversine distance as a heuristic to converge faster. -.. py:function:: gds.shortestPath.astar.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.astar.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.shortestPath.astar.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.shortestPath.astar.stream(G: Graph, **config: Any) -> pandas.DataFrame The A* shortest path algorithm computes the shortest path between a pair of nodes. It uses the relationship weight property to compare path lengths. In addition, this implementation uses the haversine distance as a heuristic to converge faster. -.. py:function:: gds.shortestPath.astar.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.astar.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.shortestPath.astar.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.astar.write(G: Graph, **config: Any) -> pandas.Series[Any] The A* shortest path algorithm computes the shortest path between a pair of nodes. It uses the relationship weight property to compare path lengths. In addition, this implementation uses the haversine distance as a heuristic to converge faster. -.. py:function:: gds.shortestPath.astar.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.astar.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.shortestPath.dijkstra.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.dijkstra.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Dijkstra shortest path algorithm computes the shortest (weighted) path between a pair of nodes. -.. py:function:: gds.shortestPath.dijkstra.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.dijkstra.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.shortestPath.dijkstra.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.shortestPath.dijkstra.stream(G: Graph, **config: Any) -> pandas.DataFrame The Dijkstra shortest path algorithm computes the shortest (weighted) path between a pair of nodes. -.. py:function:: gds.shortestPath.dijkstra.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.dijkstra.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.shortestPath.dijkstra.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.dijkstra.write(G: Graph, **config: Any) -> pandas.Series[Any] The Dijkstra shortest path algorithm computes the shortest (weighted) path between a pair of nodes. -.. py:function:: gds.shortestPath.dijkstra.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.dijkstra.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.shortestPath.yens.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.yens.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Yen's shortest path algorithm computes the k shortest (weighted) paths between a pair of nodes. -.. py:function:: gds.shortestPath.yens.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.yens.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.shortestPath.yens.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.shortestPath.yens.stream(G: Graph, **config: Any) -> pandas.DataFrame The Yen's shortest path algorithm computes the k shortest (weighted) paths between a pair of nodes. -.. py:function:: gds.shortestPath.yens.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.yens.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.shortestPath.yens.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.yens.write(G: Graph, **config: Any) -> pandas.Series[Any] The Yen's shortest path algorithm computes the k shortest (weighted) paths between a pair of nodes. -.. py:function:: gds.shortestPath.yens.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.shortestPath.yens.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.sllpa.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.sllpa.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph. -.. py:function:: gds.sllpa.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.sllpa.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.sllpa.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.sllpa.stats(G: Graph, **config: Any) -> pandas.Series[Any] The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph. -.. py:function:: gds.sllpa.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.sllpa.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.sllpa.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.sllpa.stream(G: Graph, **config: Any) -> pandas.DataFrame The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph. -.. py:function:: gds.sllpa.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.sllpa.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.sllpa.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.sllpa.write(G: Graph, **config: Any) -> pandas.Series[Any] The Speaker Listener Label Propagation algorithm is a fast algorithm for finding overlapping communities in a graph. -.. py:function:: gds.sllpa.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.sllpa.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.spanningTree.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.spanningTree.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The spanning tree algorithm visits all nodes that are in the same connected component as the starting node, and returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized. -.. py:function:: gds.spanningTree.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.spanningTree.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.spanningTree.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.spanningTree.stats(G: Graph, **config: Any) -> pandas.Series[Any] The spanning tree algorithm visits all nodes that are in the same connected component as the starting node, and returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized. -.. py:function:: gds.spanningTree.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.spanningTree.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.spanningTree.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.spanningTree.stream(G: Graph, **config: Any) -> pandas.DataFrame The spanning tree algorithm visits all nodes that are in the same connected component as the starting node, and returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized. -.. py:function:: gds.spanningTree.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.spanningTree.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.spanningTree.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.spanningTree.write(G: Graph, **config: Any) -> pandas.Series[Any] The spanning tree algorithm visits all nodes that are in the same connected component as the starting node, and returns a spanning tree of all nodes in the component where the total weight of the relationships is either minimized or maximized. -.. py:function:: gds.spanningTree.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.spanningTree.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.steinerTree.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.steinerTree.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The steiner tree algorithm accepts a source node, as well as a list of target nodes. It then attempts to find a spanning tree where there is a path from the source node to each target node, such that the total weight of the relationships is as low as possible. -.. py:function:: gds.steinerTree.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.steinerTree.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.steinerTree.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.steinerTree.stats(G: Graph, **config: Any) -> pandas.Series[Any] The steiner tree algorithm accepts a source node, as well as a list of target nodes. It then attempts to find a spanning tree where there is a path from the source node to each target node, such that the total weight of the relationships is as low as possible. -.. py:function:: gds.steinerTree.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.steinerTree.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.steinerTree.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.steinerTree.stream(G: Graph, **config: Any) -> pandas.DataFrame The steiner tree algorithm accepts a source node, as well as a list of target nodes. It then attempts to find a spanning tree where there is a path from the source node to each target node, such that the total weight of the relationships is as low as possible. -.. py:function:: gds.steinerTree.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.steinerTree.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.steinerTree.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.steinerTree.write(G: Graph, **config: Any) -> pandas.Series[Any] The steiner tree algorithm accepts a source node, as well as a list of target nodes. It then attempts to find a spanning tree where there is a path from the source node to each target node, such that the total weight of the relationships is as low as possible. -.. py:function:: gds.steinerTree.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.steinerTree.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.triangleCount.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.triangleCount.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Triangle counting is a community detection graph algorithm that is used to determine the number of triangles passing through each node in the graph. -.. py:function:: gds.triangleCount.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.triangleCount.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.triangleCount.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.triangleCount.stats(G: Graph, **config: Any) -> pandas.Series[Any] Triangle counting is a community detection graph algorithm that is used to determine the number of triangles passing through each node in the graph. -.. py:function:: gds.triangleCount.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.triangleCount.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.triangleCount.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.triangleCount.stream(G: Graph, **config: Any) -> pandas.DataFrame Triangle counting is a community detection graph algorithm that is used to determine the number of triangles passing through each node in the graph. -.. py:function:: gds.triangleCount.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.triangleCount.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.triangleCount.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.triangleCount.write(G: Graph, **config: Any) -> pandas.Series[Any] Triangle counting is a community detection graph algorithm that is used to determine the number of triangles passing through each node in the graph. -.. py:function:: gds.triangleCount.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.triangleCount.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Triangle counting is a community detection graph algorithm that is used to determine the number of triangles passing through each node in the graph. -.. py:function:: gds.triangles(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.triangles(G: Graph, **config: Any) -> pandas.DataFrame Triangles streams the nodeIds of each triangle in the graph. -.. py:function:: gds.wcc.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.wcc.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The WCC algorithm finds sets of connected nodes in an undirected graph, where all nodes in the same set form a connected component. -.. py:function:: gds.wcc.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.wcc.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.wcc.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.wcc.stats(G: Graph, **config: Any) -> pandas.Series[Any] Executes the algorithm and returns result statistics without writing the result to Neo4j. -.. py:function:: gds.wcc.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.wcc.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.wcc.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.wcc.stream(G: Graph, **config: Any) -> pandas.DataFrame The WCC algorithm finds sets of connected nodes in an undirected graph, where all nodes in the same set form a connected component. -.. py:function:: gds.wcc.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.wcc.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.wcc.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.wcc.write(G: Graph, **config: Any) -> pandas.Series[Any] The WCC algorithm finds sets of connected nodes in an undirected graph, where all nodes in the same set form a connected component. -.. py:function:: gds.wcc.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.wcc.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. diff --git a/doc/sphinx/source/conf.py b/doc/sphinx/source/conf.py index 6fca87e84..dcd6ff8d9 100644 --- a/doc/sphinx/source/conf.py +++ b/doc/sphinx/source/conf.py @@ -69,7 +69,6 @@ def setup(app): # type: ignore "pandas": ("https://pandas.pydata.org/docs/", None), "pyarrow": ("https://arrow.apache.org/docs/", None), "networkx": ("https://networkx.org/documentation/stable/", None), - "nx": ("https://networkx.org/documentation/stable/", None), } rst_epilog = """ diff --git a/doc/sphinx/source/graph.rst b/doc/sphinx/source/graph.rst index 04d07a5d2..8231cf8bb 100644 --- a/doc/sphinx/source/graph.rst +++ b/doc/sphinx/source/graph.rst @@ -5,7 +5,7 @@ This includes all the methods for projecting, deleting, and listing graphs, as w These all assume that an object of :class:`.GraphDataScience` is available as `gds`. -.. py:function:: gds.graph.construct(graph_name: str, nodes: Union[DataFrame, List[DataFrame]], relationships: Optional[Union[DataFrame, List[DataFrame]]] = None, concurrency: int = 4, undirected_relationship_types: Optional[List[str]] = None) -> Graph +.. py:function:: gds.graph.construct(graph_name: str, nodes: Union[pandas.DataFrame, List[pandas.DataFrame]], relationships: Optional[Union[pandas.DataFrame, List[pandas.DataFrame]]] = None, concurrency: int = 4, undirected_relationship_types: Optional[List[str]] = None) -> Graph Constructs a new graph in the graph catalog, using the provided node and relationship data frames. @@ -13,34 +13,34 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Gets a graph object representing a graph in the graph catalog. -.. py:function:: gds.alpha.graph.graphProperty.drop(G: Graph, graph_property: str, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.graph.graphProperty.drop(G: Graph, graph_property: str, **config: Any) -> pandas.Series[Any] Removes a graph property from a projected graph. -.. py:function:: gds.alpha.graph.graphProperty.stream(G: Graph, graph_property: str, **config: Any) -> DataFrame +.. py:function:: gds.alpha.graph.graphProperty.stream(G: Graph, graph_property: str, **config: Any) -> pandas.DataFrame Streams the given graph property. -.. py:function:: gds.alpha.graph.nodeLabel.mutate(G: Graph, node_label: str, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.graph.nodeLabel.mutate(G: Graph, node_label: str, **config: Any) -> pandas.Series[Any] Mutates the in-memory graph with the given node Label. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.graph.nodeLabel.mutate` instead. -.. py:function:: gds.alpha.graph.nodeLabel.write(G: Graph, node_label: str, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.graph.nodeLabel.write(G: Graph, node_label: str, **config: Any) -> pandas.Series[Any] Writes the given node Label to an online Neo4j database. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.graph.nodeLabel.write` instead. -.. py:function:: gds.beta.graph.export.csv(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graph.export.csv(G: Graph, **config: Any) -> pandas.Series[Any] Exports a named graph to CSV files. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.graph.export.csv` instead. -.. py:function:: gds.beta.graph.export.csv.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graph.export.csv.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Estimate the required disk space for exporting a named graph to CSV files. @@ -61,21 +61,21 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.graph.filter` instead. -.. py:function:: gds.beta.graph.relationships.stream(G: Graph, relationship_types: List[str] = ["*"], **config: Any) -> TopologyDataFrame +.. py:function:: gds.beta.graph.relationships.stream(G: Graph, relationship_types: List[str] = ["*"], **config: Any) -> Topologypandas.DataFrame Streams the given relationship source/target pairs .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.graph.relationships.stream` instead. -.. py:function:: gds.beta.graph.relationships.toUndirected(G: Graph, query: str, relationship_type: str, mutate_relationship_type: str, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graph.relationships.toUndirected(G: Graph, query: str, relationship_type: str, mutate_relationship_type: str, **config: Any) -> pandas.Series[Any] The ToUndirected procedure converts directed relationships to undirected relationships .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.graph.relationships.toUndirected` instead. -.. py:function:: gds.beta.graph.relationships.toUndirected.estimate(G: Graph, relationship_type: str, mutate_relationship_type: str, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graph.relationships.toUndirected.estimate(G: Graph, relationship_type: str, mutate_relationship_type: str, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. @@ -87,27 +87,27 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Use Cypher projection to creates a named graph in the catalog for use by algorithms. The provided query must end with a `RETURN gds.graph.project(...)` call. -.. py:function:: gds.graph.deleteRelationships(G: Graph, relationship_type: str) -> Series[Any] +.. py:function:: gds.graph.deleteRelationships(G: Graph, relationship_type: str) -> pandas.Series[Any] Delete the relationship type for a given graph stored in the graph-catalog. -.. py:function:: gds.graph.drop(G: Union[Graph | str], failIfMissing: bool = False,dbName: str = "",username: Optional[str] = None,) -> Optional[Series[Any]] +.. py:function:: gds.graph.drop(G: Union[Graph | str], failIfMissing: bool = False,dbName: str = "",username: Optional[str] = None,) -> Optional[pandas.Series[Any]] Drops a named graph from the catalog and frees up the resources it occupies. -.. py:function:: gds.graph.exists(graph_name: str) -> Series[Any] +.. py:function:: gds.graph.exists(graph_name: str) -> pandas.Series[Any] Checks if a graph exists in the catalog. -.. py:function:: gds.graph.export(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.graph.export(G: Graph, **config: Any) -> pandas.Series[Any] Exports a named graph into a new offline Neo4j database. -.. py:function:: gds.graph.export.csv(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.graph.export.csv(G: Graph, **config: Any) -> pandas.Series[Any] Exports a named graph to CSV files. -.. py:function:: gds.graph.export.csv.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.graph.export.csv.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Estimate the required disk space for exporting a named graph to CSV files. @@ -115,31 +115,31 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Computes a random graph, which will be stored in the graph catalog. -.. py:function:: gds.graph.graphProperty.drop(G: Graph, graph_property: str, **config: Any) -> Series[Any] +.. py:function:: gds.graph.graphProperty.drop(G: Graph, graph_property: str, **config: Any) -> pandas.Series[Any] Removes a graph property from a projected graph. -.. py:function:: gds.graph.graphProperty.stream(G: Graph, graph_property: str, **config: Any) -> DataFrame +.. py:function:: gds.graph.graphProperty.stream(G: Graph, graph_property: str, **config: Any) -> pandas.DataFrame Streams the given graph property. -.. py:function:: gds.graph.list(G: Optional[Union[Graph, str]] = None) -> DataFrame +.. py:function:: gds.graph.list(G: Optional[Union[Graph, str]] = None) -> pandas.DataFrame Lists information about named graphs stored in the catalog. -.. py:function:: gds.graph.nodeProperties.drop(G: Graph, node_properties: List[str], **config: Any) -> Series[Any] +.. py:function:: gds.graph.nodeProperties.drop(G: Graph, node_properties: List[str], **config: Any) -> pandas.Series[Any] Removes node properties from a projected graph. -.. py:function:: gds.graph.nodeProperties.stream(G: Graph,node_properties: Union[List[str], str],node_labels: Strings = ["*"],separate_property_columns: bool = False, db_node_properties: List[str] = [], **config: Any,) -> DataFrame +.. py:function:: gds.graph.nodeProperties.stream(G: Graph,node_properties: Union[List[str], str],node_labels: Strings = ["*"],separate_property_columns: bool = False, db_node_properties: List[str] = [], **config: Any,) -> pandas.DataFrame Streams the given node properties. -.. py:function:: gds.graph.nodeProperties.write(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], **config: Any) -> Series[Any] +.. py:function:: gds.graph.nodeProperties.write(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], **config: Any) -> pandas.Series[Any] Writes the given node properties to an online Neo4j database. -.. py:function:: gds.graph.nodeProperty.stream(G: Graph, node_properties: str, node_labels: Strings = ["*"], db_node_properties: List[str] = [], **config: Any) -> DataFrame +.. py:function:: gds.graph.nodeProperty.stream(G: Graph, node_properties: str, node_labels: Strings = ["*"], db_node_properties: List[str] = [], **config: Any) -> pandas.DataFrame Streams the given node property. @@ -151,46 +151,46 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Creates a named graph in the catalog for use by algorithms. -.. py:function:: gds.graph.project.cypher.estimate(node_projection: Any, relationship_projection: Any, **config: Any) -> Series[Any] +.. py:function:: gds.graph.project.cypher.estimate(node_projection: Any, relationship_projection: Any, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.graph.project.estimate(node_projection: Any, relationship_projection: Any, **config: Any) -> Series[Any] +.. py:function:: gds.graph.project.estimate(node_projection: Any, relationship_projection: Any, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.graph.filter` instead. -.. py:function:: gds.graph.relationships.stream(G: Graph, relationship_types: List[str] = ["*"], **config: Any) -> TopologyDataFrame +.. py:function:: gds.graph.relationships.stream(G: Graph, relationship_types: List[str] = ["*"], **config: Any) -> Topologypandas.DataFrame Streams the given relationship source/target pairs -.. py:function:: gds.graph.relationships.toUndirected(G: Graph, query: str, relationship_type: str, mutate_relationship_type: str, **config: Any) -> Series[Any] +.. py:function:: gds.graph.relationships.toUndirected(G: Graph, query: str, relationship_type: str, mutate_relationship_type: str, **config: Any) -> pandas.Series[Any] The ToUndirected procedure converts directed relationships to undirected relationships -.. py:function:: gds.graph.relationships.toUndirected.estimate(G: Graph, relationship_type: str, mutate_relationship_type: str, **config: Any) -> Series[Any] +.. py:function:: gds.graph.relationships.toUndirected.estimate(G: Graph, relationship_type: str, mutate_relationship_type: str, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.graph.relationship.write(G: Graph, relationship_type: str, relationship_property: str = "", **config: Any) -> Series[Any] +.. py:function:: gds.graph.relationship.write(G: Graph, relationship_type: str, relationship_property: str = "", **config: Any) -> pandas.Series[Any] Writes the given relationship and an optional relationship property to an online Neo4j database. -.. py:function:: gds.graph.relationshipProperties.stream(G: Graph, relationship_properties: List[str],relationship_types: Union[str, List[str]] = ["*"],separate_property_columns: bool = False,**config: Any,) -> DataFrame +.. py:function:: gds.graph.relationshipProperties.stream(G: Graph, relationship_properties: List[str],relationship_types: Union[str, List[str]] = ["*"],separate_property_columns: bool = False,**config: Any,) -> pandas.DataFrame Streams the given relationship properties. -.. py:function:: gds.graph.relationshipProperties.write(G: Graph, relationship_type: str, relationship_properties: List[str], **config: Any,) -> DataFrame +.. py:function:: gds.graph.relationshipProperties.write(G: Graph, relationship_type: str, relationship_properties: List[str], **config: Any,) -> pandas.DataFrame Write the given relationship properties back to the database. -.. py:function:: gds.graph.relationshipProperty.stream(G: Graph, node_properties: str, node_labels: Union[str, List[str]] = ["*"], **config: Any) -> DataFrame +.. py:function:: gds.graph.relationshipProperty.stream(G: Graph, node_properties: str, node_labels: Union[str, List[str]] = ["*"], **config: Any) -> pandas.DataFrame Streams the given relationship property. -.. py:function:: gds.graph.relationships.drop(G: Graph, relationship_type: str,) -> Series[Any] +.. py:function:: gds.graph.relationships.drop(G: Graph, relationship_type: str,) -> pandas.Series[Any] Delete the relationship type for a given graph stored in the graph-catalog. @@ -198,27 +198,27 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Removes node properties from a projected graph. -.. py:function:: gds.graph.streamNodeProperties(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], separate_property_columns: bool = False, **config: Any) -> DataFrame +.. py:function:: gds.graph.streamNodeProperties(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], separate_property_columns: bool = False, **config: Any) -> pandas.DataFrame Streams the given node properties. -.. py:function:: gds.graph.streamNodeProperty(G: Graph, node_properties: str, node_labels: Strings = ["*"], **config: Any) -> DataFrame +.. py:function:: gds.graph.streamNodeProperty(G: Graph, node_properties: str, node_labels: Strings = ["*"], **config: Any) -> pandas.DataFrame Streams the given node property. -.. py:function:: gds.graph.streamRelationshipProperties(G: Graph, relationship_properties: List[str], relationship_types: Strings = ["*"], separate_property_columns: bool = False, **config: Any) -> DataFrame +.. py:function:: gds.graph.streamRelationshipProperties(G: Graph, relationship_properties: List[str], relationship_types: Strings = ["*"], separate_property_columns: bool = False, **config: Any) -> pandas.DataFrame Streams the given relationship properties. -.. py:function:: gds.graph.streamRelationshipProperty(G: Graph, relationship_properties: str, relationship_types: Strings = ["*"], **config: Any) -> DataFrame +.. py:function:: gds.graph.streamRelationshipProperty(G: Graph, relationship_properties: str, relationship_types: Strings = ["*"], **config: Any) -> pandas.DataFrame Streams the given relationship property. -.. py:function:: gds.graph.writeNodeProperties(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], **config: Any) -> Series[Any] +.. py:function:: gds.graph.writeNodeProperties(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], **config: Any) -> pandas.Series[Any] Writes the given node properties to an online Neo4j database. -.. py:function:: gds.graph.writeRelationship(G: Graph, relationship_type: str, relationship_property: str = "", **config: Any) -> Series[Any] +.. py:function:: gds.graph.writeRelationship(G: Graph, relationship_type: str, relationship_property: str = "", **config: Any) -> pandas.Series[Any] Writes the given relationship and an optional relationship property to an online Neo4j database. @@ -246,7 +246,7 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Loads a OGBL dataset into a named graph in the catalog for use by algorithms. -.. py:function:: gds.graph.networkx.load(nx_G: nx.Graph, graph_name: str, concurrency: int = 4) -> Graph +.. py:function:: gds.graph.networkx.load(nx_G: networkx.Graph, graph_name: str, concurrency: int = 4) -> Graph Loads a NetworkX graph into a named graph in the catalog for use by algorithms. @@ -254,10 +254,10 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Finds a node id by its labels and properties. -.. py:function:: gds.graph.nodeLabel.mutate(G: Graph, node_label: str, **config: Any) -> Series[Any] +.. py:function:: gds.graph.nodeLabel.mutate(G: Graph, node_label: str, **config: Any) -> pandas.Series[Any] Mutates the in-memory graph with the given node Label. -.. py:function:: gds.graph.nodeLabel.write(G: Graph, node_label: str, **config: Any) -> Series[Any] +.. py:function:: gds.graph.nodeLabel.write(G: Graph, node_label: str, **config: Any) -> pandas.Series[Any] Writes the given node Label to an online Neo4j database. .. py:function:: gds.graph.filter(graph_name: str, from_G: Graph, node_filter: str, relationship_filter: str, **config: Any,) -> GraphCreateResult diff --git a/doc/sphinx/source/misc.rst b/doc/sphinx/source/misc.rst index 21c9aa4f7..7bc4b1baa 100644 --- a/doc/sphinx/source/misc.rst +++ b/doc/sphinx/source/misc.rst @@ -5,7 +5,7 @@ This includes procedures for configuring the library. These all assume that an object of :class:`.GraphDataScience` is available as `gds`. -.. py:function:: gds.alpha.config.defaults.list(key: Optional[str] = None, username: Optional[str] = None) -> DataFrame +.. py:function:: gds.alpha.config.defaults.list(key: Optional[str] = None, username: Optional[str] = None) -> pandas.DataFrame List defaults; global by default, but also optionally for a specific user and/ or key .. deprecated:: 2.5.0 @@ -17,7 +17,7 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.config.defaults.set` instead. -.. py:function:: gds.alpha.config.limits.list(key: Optional[str] = None, username: Optional[str] = None) -> DataFrame +.. py:function:: gds.alpha.config.limits.list(key: Optional[str] = None, username: Optional[str] = None) -> pandas.DataFrame List limits; global by default, but also optionally for a specific user and/ or key .. deprecated:: 2.5.0 @@ -28,7 +28,7 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Set a limit; global by, default, but also optionally for a specific user .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.config.limits.set` instead. -.. py:function:: gds.config.defaults.list(key: Optional[str] = None, username: Optional[str] = None) -> DataFrame +.. py:function:: gds.config.defaults.list(key: Optional[str] = None, username: Optional[str] = None) -> pandas.DataFrame List defaults; global by default, but also optionally for a specific user and/ or key @@ -36,7 +36,7 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Set a default; global by, default, but also optionally for a specific user -.. py:function:: gds.config.limits.list(key: Optional[str] = None, username: Optional[str] = None) -> DataFrame +.. py:function:: gds.config.limits.list(key: Optional[str] = None, username: Optional[str] = None) -> pandas.DataFrame List limits; global by default, but also optionally for a specific user and/ or key @@ -49,21 +49,21 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.systemMonitor` instead. -.. py:function:: gds.alpha.userLog() -> DataFrame +.. py:function:: gds.alpha.userLog() -> pandas.DataFrame Log warnings and hints for currently running tasks. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.userLog` instead. -.. py:function:: gds.beta.listProgress(job_id: Optional[str] = None) -> DataFrame +.. py:function:: gds.beta.listProgress(job_id: Optional[str] = None) -> pandas.DataFrame List progress events for currently running tasks. .. deprecated:: 2.5.0 - Since GDS server version 2.5.0 you should use the endpoint :func:`gds.listProgress` instead. + Since GDS server version 2.5.0 you should use the endpoint :func:`gds.listProgress` instead. .. py:function:: gds.systemMonitor() -> Series[Any] Get an overview of the system's workload and available resources -.. py:function:: gds.listProgress(job_id: Optional[str] = None) -> DataFrame +.. py:function:: gds.listProgress(job_id: Optional[str] = None) -> pandas.DataFrame List progress events for currently running tasks. @@ -111,5 +111,5 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Return True if the graph data science library is licensed. -.. py:function:: gds.userLog() -> DataFrame +.. py:function:: gds.userLog() -> pandas.DataFrame Log warnings and hints for currently running tasks. diff --git a/doc/sphinx/source/ml.rst b/doc/sphinx/source/ml.rst index f1fd6df5d..636b47a77 100644 --- a/doc/sphinx/source/ml.rst +++ b/doc/sphinx/source/ml.rst @@ -9,190 +9,190 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Get a pipeline object representing a pipeline in the Pipeline Catalog. -.. py:function:: gds.alpha.ml.splitRelationships.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.ml.splitRelationships.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Splits a graph into holdout and remaining relationship types and adds them to the graph. -.. py:function:: gds.alpha.ml.splitRelationships.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.alpha.ml.splitRelationships.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Splits a graph into holdout and remaining relationship types and adds them to the graph. -.. py:function:: gds.alpha.pipeline.nodeRegression.create(name: str) -> Tuple[NRTrainingPipeline, Series[Any]] +.. py:function:: gds.alpha.pipeline.nodeRegression.create(name: str) -> Tuple[NRTrainingPipeline, pandas.Series[Any]] Creates a node regression training pipeline in the pipeline catalog. -.. py:function:: gds.beta.graphSage.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graphSage.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods. -.. py:function:: gds.beta.graphSage.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graphSage.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods. -.. py:function:: gds.beta.graphSage.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.graphSage.stream(G: Graph, **config: Any) -> pandas.DataFrame The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods. -.. py:function:: gds.beta.graphSage.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graphSage.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.graphSage.train(G: Graph, **config: Any) -> Tuple[MODEL_TYPE, Series[Any]] +.. py:function:: gds.beta.graphSage.train(G: Graph, **config: Any) -> Tuple[MODEL_TYPE, pandas.Series[Any]] The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods. -.. py:function:: gds.beta.graphSage.train.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graphSage.train.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.graphSage.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graphSage.write(G: Graph, **config: Any) -> pandas.Series[Any] The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods. -.. py:function:: gds.beta.graphSage.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.graphSage.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hashgnn.mutate` instead. -.. py:function:: gds.beta.hashgnn.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.hashgnn.mutate(G: Graph, **config: Any) -> pandas.Series[Any] HashGNN creates node embeddings by hashing and message passing. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hashgnn.mutate.estimate` instead. -.. py:function:: gds.beta.hashgnn.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.hashgnn.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] HashGNN creates node embeddings by hashing and message passing. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hashgnn.stream` instead. -.. py:function:: gds.beta.hashgnn.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.hashgnn.stream(G: Graph, **config: Any) -> pandas.DataFrame HashGNN creates node embeddings by hashing and message passing. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.hashgnn.stream.estimate` instead. -.. py:function:: gds.beta.hashgnn.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.hashgnn.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] HashGNN creates node embeddings by hashing and message passing. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.node2vec.mutate` instead. -.. py:function:: gds.beta.node2vec.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.node2vec.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Node2Vec algorithm computes embeddings for nodes based on random walks. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.node2vec.mutate.estimate` instead. -.. py:function:: gds.beta.node2vec.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.node2vec.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.node2vec.stream` instead. -.. py:function:: gds.beta.node2vec.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.beta.node2vec.stream(G: Graph, **config: Any) -> pandas.DataFrame The Node2Vec algorithm computes embeddings for nodes based on random walks. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.node2vec.stream.estimate` instead. -.. py:function:: gds.beta.node2vec.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.node2vec.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.node2vec.write` instead. -.. py:function:: gds.beta.node2vec.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.node2vec.write(G: Graph, **config: Any) -> pandas.Series[Any] The Node2Vec algorithm computes embeddings for nodes based on random walks. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.node2vec.write.estimate` instead. -.. py:function:: gds.beta.node2vec.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.beta.node2vec.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.beta.pipeline.drop(pipeline: TrainingPipeline[PipelineModel]) -> Series[Any] +.. py:function:: gds.beta.pipeline.drop(pipeline: TrainingPipeline[PipelineModel]) -> pandas.Series[Any] Drops a pipeline and frees up the resources it occupies. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.pipeline.drop` instead. -.. py:function:: gds.beta.pipeline.exists(pipeline_name: str) -> Series[Any] +.. py:function:: gds.beta.pipeline.exists(pipeline_name: str) -> pandas.Series[Any] Checks if a given pipeline exists in the pipeline catalog. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.pipeline.exists` instead. -.. py:function:: gds.beta.pipeline.list(pipeline: Optional[TrainingPipeline[PipelineModel]] = None) -> DataFrame +.. py:function:: gds.beta.pipeline.list(pipeline: Optional[TrainingPipeline[PipelineModel]] = None) -> pandas.DataFrame Lists all pipelines contained in the pipeline catalog. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.pipeline.list` instead. -.. py:function:: gds.pipeline.drop(pipeline: TrainingPipeline[PipelineModel]) -> Series[Any] +.. py:function:: gds.pipeline.drop(pipeline: TrainingPipeline[PipelineModel]) -> pandas.Series[Any] Drops a pipeline and frees up the resources it occupies. -.. py:function:: gds.pipeline.exists(pipeline_name: str) -> Series[Any] +.. py:function:: gds.pipeline.exists(pipeline_name: str) -> pandas.Series[Any] Checks if a given pipeline exists in the pipeline catalog. -.. py:function:: gds.pipeline.list(pipeline: Optional[TrainingPipeline[PipelineModel]] = None) -> DataFrame +.. py:function:: gds.pipeline.list(pipeline: Optional[TrainingPipeline[PipelineModel]] = None) -> pandas.DataFrame Lists all pipelines contained in the pipeline catalog. -.. py:function:: gds.beta.pipeline.linkPrediction.create(name: str) -> Tuple[LPTrainingPipeline, Series[Any]] +.. py:function:: gds.beta.pipeline.linkPrediction.create(name: str) -> Tuple[LPTrainingPipeline, pandas.Series[Any]] Creates a link prediction pipeline in the pipeline catalog. -.. py:function:: gds.beta.pipeline.nodeClassification.create(name: str) -> Tuple[NCTrainingPipeline, Series[Any]] +.. py:function:: gds.beta.pipeline.nodeClassification.create(name: str) -> Tuple[NCTrainingPipeline, pandas.Series[Any]] Creates a node classification training pipeline in the pipeline catalog. -.. py:function:: gds.fastRP.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.fastRP.mutate(G: Graph, **config: Any) -> pandas.Series[Any] Random Projection produces node embeddings via the fastrp algorithm -.. py:function:: gds.fastRP.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.fastRP.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Random Projection produces node embeddings via the fastrp algorithm -.. py:function:: gds.fastRP.stats(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.fastRP.stats(G: Graph, **config: Any) -> pandas.Series[Any] Random Projection produces node embeddings via the fastrp algorithm -.. py:function:: gds.fastRP.stats.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.fastRP.stats.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Random Projection produces node embeddings via the fastrp algorithm -.. py:function:: gds.fastRP.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.fastRP.stream(G: Graph, **config: Any) -> pandas.DataFrame Random Projection produces node embeddings via the fastrp algorithm -.. py:function:: gds.fastRP.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.fastRP.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Random Projection produces node embeddings via the fastrp algorithm -.. py:function:: gds.fastRP.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.fastRP.write(G: Graph, **config: Any) -> pandas.Series[Any] Random Projection produces node embeddings via the fastrp algorithm -.. py:function:: gds.fastRP.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.fastRP.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Random Projection produces node embeddings via the fastrp algorithm @@ -200,50 +200,50 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Return a list of selected values in a one hot encoding format. -.. py:function:: gds.hashgnn.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.hashgnn.mutate(G: Graph, **config: Any) -> pandas.Series[Any] HashGNN creates node embeddings by hashing and message passing. -.. py:function:: gds.hashgnn.mutate.estimate(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.hashgnn.mutate.estimate(G: Graph, **config: Any) -> pandas.DataFrame Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.hashgnn.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.hashgnn.stream(G: Graph, **config: Any) -> pandas.DataFrame HashGNN creates node embeddings by hashing and message passing. -.. py:function:: gds.hashgnn.stream.estimate(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.hashgnn.stream.estimate(G: Graph, **config: Any) -> pandas.DataFrame Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.hashgnn.write(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.hashgnn.write(G: Graph, **config: Any) -> pandas.DataFrame HashGNN creates node embeddings by hashing and message passing. -.. py:function:: gds.hashgnn.write.estimate(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.hashgnn.write.estimate(G: Graph, **config: Any) -> pandas.DataFrame Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.node2vec.mutate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.node2vec.mutate(G: Graph, **config: Any) -> pandas.Series[Any] The Node2Vec algorithm computes embeddings for nodes based on random walks. -.. py:function:: gds.node2vec.mutate.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.node2vec.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.node2vec.stream(G: Graph, **config: Any) -> DataFrame +.. py:function:: gds.node2vec.stream(G: Graph, **config: Any) -> pandas.DataFrame The Node2Vec algorithm computes embeddings for nodes based on random walks. -.. py:function:: gds.node2vec.stream.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.node2vec.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. -.. py:function:: gds.node2vec.write(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.node2vec.write(G: Graph, **config: Any) -> pandas.Series[Any] The Node2Vec algorithm computes embeddings for nodes based on random walks. -.. py:function:: gds.node2vec.write.estimate(G: Graph, **config: Any) -> Series[Any] +.. py:function:: gds.node2vec.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any] Returns an estimation of the memory consumption for that procedure. diff --git a/doc/sphinx/source/model.rst b/doc/sphinx/source/model.rst index d701180a3..1ce4049ad 100644 --- a/doc/sphinx/source/model.rst +++ b/doc/sphinx/source/model.rst @@ -7,15 +7,15 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.backup` instead. -.. py:function:: gds.alpha.backup(**config) -> DataFrame +.. py:function:: gds.alpha.backup(**config) -> pandas.DataFrame The back-up procedure persists graphs and models to disk -.. py:function:: gds.alpha.model.delete(model: Model) -> Series[Any] +.. py:function:: gds.alpha.model.delete(model: Model) -> pandas.Series[Any] Deletes a stored model from disk. -.. py:function:: gds.alpha.model.load(model_name: str) -> Tuple[Model, Series[Any]] +.. py:function:: gds.alpha.model.load(model_name: str) -> Tuple[Model, pandas.Series[Any]] Load a stored model into main memory. @@ -23,30 +23,30 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Make a trained model accessible by all users. -.. py:function:: gds.alpha.model.store(model: Model, failIfUnsupportedType: bool = True) -> Series[Any] +.. py:function:: gds.alpha.model.store(model: Model, failIfUnsupportedType: bool = True) -> pandas.Series[Any] Store the selected model to disk. .. deprecated:: 2.5.0 Since GDS server version 2.5.0 you should use the endpoint :func:`gds.restore` instead. -.. py:function:: gds.alpha.restore(**config: Any) -> DataFrame +.. py:function:: gds.alpha.restore(**config: Any) -> pandas.DataFrame The restore procedure reads graphs and models from disk. -.. py:function:: gds.backup(**config) -> DataFrame +.. py:function:: gds.backup(**config) -> pandas.DataFrame The back-up procedure persists graphs and models to disk -.. py:function:: gds.beta.model.drop(model: Model) -> Series[Any] +.. py:function:: gds.beta.model.drop(model: Model) -> pandas.Series[Any] Drops a loaded model and frees up the resources it occupies. -.. py:function:: gds.beta.model.exists(model_name: str) -> Series[Any] +.. py:function:: gds.beta.model.exists(model_name: str) -> pandas.Series[Any] Checks if a given model exists in the model catalog. -.. py:function:: gds.beta.model.list(model: Optional[Model] = None) -> DataFrame +.. py:function:: gds.beta.model.list(model: Optional[Model] = None) -> pandas.DataFrame Lists all models contained in the model catalog. @@ -54,15 +54,15 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Returns a model from the model catalog. -.. py:function:: gds.restore(**config: Any) -> DataFrame +.. py:function:: gds.restore(**config: Any) -> pandas.DataFrame The restore procedure reads graphs and models from disk. -.. py:function:: gds.model.delete(model: Model) -> Series[Any] +.. py:function:: gds.model.delete(model: Model) -> pandas.Series[Any] Deletes a stored model from disk. -.. py:function:: gds.model.load(model_name: str) -> Tuple[Model, Series[Any]] +.. py:function:: gds.model.load(model_name: str) -> Tuple[Model, pandas.Series[Any]] Load a stored model into main memory. @@ -70,19 +70,19 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g Make a trained model accessible by all users. -.. py:function:: gds.model.store(model: Model, failIfUnsupportedType: bool = True) -> Series[Any] +.. py:function:: gds.model.store(model: Model, failIfUnsupportedType: bool = True) -> pandas.Series[Any] Store the selected model to disk. -.. py:function:: gds.model.drop(model: Model) -> Series[Any] +.. py:function:: gds.model.drop(model: Model) -> pandas.Series[Any] Drops a loaded model and frees up the resources it occupies. -.. py:function:: gds.model.exists(model_name: str) -> Series[Any] +.. py:function:: gds.model.exists(model_name: str) -> pandas.Series[Any] Checks if a given model exists in the model catalog. -.. py:function:: gds.model.list(model: Optional[Model] = None) -> DataFrame +.. py:function:: gds.model.list(model: Optional[Model] = None) -> pandas.DataFrame Lists all models contained in the model catalog. diff --git a/doc/sphinx/source/themes/neo4j/static/css/neo4j.css b/doc/sphinx/source/themes/neo4j/static/css/neo4j.css index 31e91e4eb..a9093b203 100644 --- a/doc/sphinx/source/themes/neo4j/static/css/neo4j.css +++ b/doc/sphinx/source/themes/neo4j/static/css/neo4j.css @@ -30,6 +30,14 @@ body.sphinx .content .search { background: initial; } +body.sphinx a.external::after { + display: inline-block; + content: ""; + padding: 0; + margin: 0; + background-position: 50%; +} + @media screen and (min-width: 1024px) { body.sphinx .toolbar { @@ -50,7 +58,7 @@ body.sphinx .content .search { body.sphinx .nav-container { border: none; } - + body.sphinx .nav, body.sphinx .nav-panel-menu { height: auto; @@ -60,11 +68,11 @@ body.sphinx .content .search { body.sphinx .sphinxsidebar { background: #f5f7fa; } - + body.sphinx .nav-panel-menu:not(.is-active)::after { background: initial; } - + body.sphinx .navbar-start { display: -webkit-box; display: -ms-flexbox; @@ -78,7 +86,7 @@ body.sphinx .content .search { align-items: center; margin-right: 2rem; } - + body.sphinx .navbar-start .navbar-link { margin-left: auto; } @@ -216,7 +224,7 @@ body.sphinx .deprecated > *::after { content: unset; } -body.sphinx .doc dl.deprecated dt:first-of-type span.sig-prename, +body.sphinx .doc dl.deprecated dt:first-of-type span.sig-prename, body.sphinx .doc dl.deprecated dt:first-of-type span.sig-name { border-bottom: 1px solid #f6ad55; /* color: #702459; */