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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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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

Betweenness centrality

Betweenness is another way to measure centrality. Instead of summing the distances, we are now counting the number of shortest paths traversing a given node:

Cn = ∑ σ(u, v | n) / ∑ σ(u, v)

Here, σ(u, v) is the number of shortest paths between u and v, and σ(u, v | n) is the number of such paths passing through n.

This measure is particularly useful for identifying critical nodes in a network, such as bridges in a road network.

It is used in the following way with GDS:

CALL gds.alpha.betweenness.stream("projected_graph", {})
YIELD nodeId, centrality as score
RETURN gds.util.asNode(nodeId).name, score
ORDER BY score DESC
If you are using GDS ≥ 1.3, then the betweenness centrality procedures have been moved to the production tier and are hence named
gds.betweenness.stream and gds.betweenness.write.
You can check which version of GDS you are using with the RETURN gds.version() Cypher code.

Here are the sorted...

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