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Nov 14, 2025
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API ref add links to other docs #1007

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7f19383
Support linking to pandas, neo4j and co in sphinx
FlorentinD Nov 13, 2025
648ecf1
Link to pandas and nx classes in typed-out ref docs
FlorentinD Nov 13, 2025
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880 changes: 440 additions & 440 deletions doc/sphinx/algorithms.json
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880 changes: 440 additions & 440 deletions doc/sphinx/source/algorithms.rst
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11 changes: 11 additions & 0 deletions doc/sphinx/source/conf.py
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Expand Up @@ -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
Expand Down Expand Up @@ -60,6 +61,16 @@ 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),
}

rst_epilog = """
.. |api-version| replace:: {versionnum}
""".format(
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84 changes: 42 additions & 42 deletions doc/sphinx/source/graph.rst
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18 changes: 9 additions & 9 deletions doc/sphinx/source/misc.rst
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Expand Up @@ -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
Expand All @@ -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
Expand All @@ -28,15 +28,15 @@ 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

.. py:function:: gds.config.defaults.set(key: str, value: Any, username: Optional[str] = None) -> None

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

Expand All @@ -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.

Expand Down Expand Up @@ -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.
98 changes: 49 additions & 49 deletions doc/sphinx/source/ml.rst
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Expand Up @@ -9,241 +9,241 @@ 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

.. py:function:: gds.alpha.ml.oneHotEncoding(available_values: List[Any], selected_values: List[Any]) -> List[int]

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.
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