refactoring intro, named graphs, projected graph model (#850)

* refactoring intro, named graphs, projected graph model

* more explanation around named graphs

* make the projected graph pages in their own right

* explain the label + relationship type projection

* explain duplicate rels

* the text sounded confusing - rewording

* move named graph under projected graph model and added installation instructions for Neo4j Desktop

* section instead of chapter

* move named graph out of intro toc

* edit proj graph section

* edit cypher proj page

* newer image for projected graph model

* formatting

Author: mneedham

doc/asciidoc/algorithms-centrality.adoc

@@ -1,5 +1,7 @@
 [[algorithms-centrality]]
+// tag::header[]
 = Centrality algorithms
+// end::header[]
 
 ifdef::env-docs[]
 [abstract]
@@ -11,6 +13,7 @@ endif::env-docs[]
 // todo introduction to graph algorithms, also say something about pregel & parallelization
 // todo sensible ordering
 
+// tag::summary[]
 The following centrality algorithms determine the importance of distinct nodes in a network:
 
 * <<algorithms-pagerank, PageRank>> (`algo.pageRank`)
@@ -20,6 +23,7 @@ The following centrality algorithms determine the importance of distinct nodes i
 * <<algorithms-harmonic-centrality, Harmonic Centrality>> (`algo.closeness.harmonic`)
 * <<algorithms-eigenvector, Eigenvector Centrality>> (`algo.eigenvector`)
 * <<algorithms-degree-centrality, Degree Centrality>> (`algo.degree`)
+// end::summary[]
 
 include::pagerank.adoc[leveloffset=2]
 

doc/asciidoc/algorithms-community.adoc

@@ -1,5 +1,7 @@
 [[algorithms-community]]
+// tag::header[]
 = Community detection algorithms
+// end::header[]
 
 ifdef::env-docs[]
 [abstract]
@@ -11,6 +13,7 @@ endif::env-docs[]
 // todo introduction to graph algorithms, also say something about pregel & parallelization
 // todo sensible ordering
 
+// tag::summary[]
 The following community detection algorithms evaluate how a group is clustered or partitioned, as well as its tendency to strengthen or break apart:
 
 * <<algorithms-louvain, Louvain>> (`algo.louvain`)
@@ -19,6 +22,7 @@ The following community detection algorithms evaluate how a group is clustered o
 * <<algorithms-strongly-connected-components, Strongly Connected Components>> (`algo.scc`)
 * <<algorithms-triangle-count-clustering-coefficient, Triangle Counting / Clustering Coefficient>> (`algo.triangleCount`)
 * <<algorithms-balanced-triads, Balanced Triads>> (`algo.balancedTriads`)
+// end::summary[]
 
 include::louvain.adoc[leveloffset=2]
 

doc/asciidoc/algorithms-link-prediction.adoc

@@ -1,5 +1,7 @@
 [[algorithms-linkprediction]]
+// tag::header[]
 = Link Prediction algorithms
+// end::header[]
 
 ifdef::env-docs[]
 [abstract]
@@ -8,6 +10,8 @@ This chapter provides explanations and examples for each of the link prediction
 --
 endif::env-docs[]
 
+// tag::summary[]
+
 These algorithms help determine the closeness of a pair of nodes.
 We would then use the computed scores as part of a link prediction solution:
 
@@ -17,6 +21,8 @@ We would then use the computed scores as part of a link prediction solution:
 * <<algorithms-linkprediction-resource-allocation, Resource Allocation>> (`algo.linkprediction.resourceAllocation`)
 * <<algorithms-linkprediction-same-community, Same Community>> (`algo.linkprediction.sameCommunity`)
 * <<algorithms-linkprediction-total-neighbors, Total Neighbors>> (`algo.linkprediction.totalNeighbors`)
+// end::summary[]
+
 
 include::linkprediction-adamic-adar.adoc[leveloffset=2]
 include::linkprediction-common-neighbors.adoc[leveloffset=2]

doc/asciidoc/algorithms-path-finding.adoc

@@ -1,5 +1,7 @@
 [[algorithms-path-finding]]
+// tag::header[]
 = Path finding algorithms
+// end::header[]
 
 ifdef::env-docs[]
 [abstract]
@@ -11,6 +13,7 @@ endif::env-docs[]
 // todo introduction to graph algorithms, also say something about pregel & parallelization
 // todo sensible ordering
 
+// tag::summary[]
 The following path finding algorithms help find the shortest path or evaluate the availability and quality of routes:
 
 * <<algorithms-minimum-weight-spanning-tree, Minimum Weight Spanning Tree>> (`algo.mst`)
@@ -20,6 +23,7 @@ The following path finding algorithms help find the shortest path or evaluate th
 * <<algorithms-a_star, A*>> (`algo.shortestPath.astar`)
 * <<algorithms-yens-k-shortest-path, Yen’s K-shortest paths>> (`algo.kShortestPaths`)
 * <<algorithms-random-walk, Random Walk>> (`algo.randomWalk`)
+// end::summary[]
 
 include::minimum-weight-spanning-tree.adoc[leveloffset=2]
 

doc/asciidoc/algorithms-preprocessing.adoc

@@ -1,5 +1,7 @@
 [[algorithms-preprocessing]]
+// tag::header[]
 = Preprocessing functions and procedures
+// end::header[]
 
 ifdef::env-docs[]
 [abstract]
@@ -9,8 +11,11 @@ This chapter provides an explanation and example for preprocessing functions and
 endif::env-docs[]
 
 //Edit this (and abstract above) when there is more than one sub-topic
+
+// tag::summary[]
 The following feature and procedure can be used as part of the data preparation process:
 
 * <<algorithms-one-hot-encoding, One Hot Encoding>> (`algo.ml.oneHotEncoding`)
+// end::summary[]
 
 include::one-hot-encoding.adoc[leveloffset=2]

doc/asciidoc/algorithms-similarity.adoc

@@ -1,5 +1,7 @@
 [[algorithms-similarity]]
+// tag::header[]
 = Similarity algorithms
+// end::header[]
 
 ifdef::env-docs[]
 [abstract]
@@ -8,13 +10,15 @@ This chapter provides explanations and examples for each of the similarity algor
 --
 endif::env-docs[]
 
+// tag::summary[]
 These algorithms help calculate the similarity of nodes:
 
 * <<algorithms-similarity-jaccard, Jaccard Similarity>> (`algo.similarity.jaccard`)
 * <<algorithms-similarity-cosine, Cosine Similarity>> (`algo.similarity.cosine`)
 * <<algorithms-similarity-pearson, Pearson Similarity>> (`algo.similarity.pearson`)
 * <<algorithms-similarity-euclidean, Euclidean Distance>> (`algo.similarity.euclidean`)
 * <<algorithms-similarity-overlap, Overlap Similarity>> (`algo.similarity.overlap`)
+// end::summary[]
 
 include::similarity-jaccard.adoc[leveloffset=2]
 include::similarity-cosine.adoc[leveloffset=2]

doc/asciidoc/articlerank.adoc

@@ -109,7 +109,7 @@ include::scripts/articlerank.cypher[tag=huge-projection]
 [[algorithms-articlerank-cp]]
 == Cypher projection
 
-include::cypher-projection.adoc[tag=explanation]
+include::projected-graph-model/cypher-projection.adoc[tag=explanation]
 
 .Set `graph:'cypher'` in the config:
 

doc/asciidoc/astar.adoc

@@ -82,7 +82,7 @@ include::scripts/astar.cypher[tag=stream-sample-graph]
 [[algorithms-a_star-cp]]
 == Cypher projection
 
-include::cypher-projection.adoc[tag=explanation]
+include::projected-graph-model/cypher-projection.adoc[tag=explanation]
 
 .Set `graph:'cypher'` in the config:
 

doc/asciidoc/betweenness-centrality.adoc

@@ -129,7 +129,7 @@ include::betweenness-centrality-ra-brandes.adoc[leveloffset=3]
 [[algorithms-betweenness-centrality-cp]]
 == Cypher projection
 
-include::cypher-projection.adoc[tag=explanation]
+include::projected-graph-model/cypher-projection.adoc[tag=explanation]
 
 .Set `graph:'cypher'` in the config:
 

doc/asciidoc/closeness-centrality.adoc

@@ -159,7 +159,7 @@ include::scripts/closeness-centrality.cypher[tag=huge-projection]
 [[algorithms-closeness-centrality-cp]]
 == Cypher projection
 
-include::cypher-projection.adoc[tag=explanation]
+include::projected-graph-model/cypher-projection.adoc[tag=explanation]
 
 .Set `graph:'cypher'` in the config: