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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

Strongly connected components

In strongly connected components, the direction of relationships matters. Each node in a component must be able to join any other node in the same component in both directions. Focusing on the nodes A to G, which were grouped in the same community by the weakly connected component algorithm, you can see that it is not always possible to go from one node to another in both directions. For instance, going from D to A is possible (through C), but going from A to D is impossible.

To see the components identified with this stronger rule, we can use the gds.alpha.scc procedure in the following way:

CALL gds.alpha.scc.stream("simple_projected_graph") 
YIELD nodeId, partition as componentId
RETURN gds.util.asNode(nodeId).name as nodeName, componentId
ORDER BY componentId

Here are the results of the preceding code:

╒══════════╤══════...
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