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Hands-On Graph Analytics with Neo4j

You're reading from   Hands-On Graph Analytics with Neo4j Perform graph processing and visualization techniques using connected data across your enterprise

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781839212611
Length 510 pages
Edition 1st Edition
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Author (1):
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 Scifo Scifo
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Scifo
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Graph Modeling with Neo4j
2. Graph Databases FREE CHAPTER 3. The Cypher Query Language 4. Empowering Your Business with Pure Cypher 5. Section 2: Graph Algorithms
6. The Graph Data Science Library and Path Finding 7. Spatial Data 8. Node Importance 9. Community Detection and Similarity Measures 10. Section 3: Machine Learning on Graphs
11. Using Graph-based Features in Machine Learning 12. Predicting Relationships 13. Graph Embedding - from Graphs to Matrices 14. Section 4: Neo4j for Production
15. Using Neo4j in Your Web Application 16. Neo4j at Scale 17. Other Books You May Enjoy

The number of components

In order to learn about the graph structure, a first common step is to identify graph components, or independent sub-graphs. To do so, we are going to use the WCC algorithm from the GDS.

In this example, we will use an anonymous projected graph:

CALL gds.wcc.write({
nodeProjection: "User",
relationshipProjection: {
FOLLOWS: {
type: "FOLLOWS",
orientation: "UNDIRECTED",
aggregation: "SINGLE"
}
},
writeProperty: "wcc"
})

This procedure does all of the following:

  • Runs the WCC algorithm.
  • Writes the results back to the graph by adding a property called wcc to each node.
  • Uses all nodes with the User label and all relationships with the FOLLOWS type, ignoring the relationship direction and taking into account one single edge between two identical nodes – if A follows B and B follows A and the graph is undirected, we would have two edges between A and B, adding the aggregation. The...
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