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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

Critical or bridging nodes

Degree-based centrality algorithms identify important nodes in terms of their connections. But what happens to our graph if node 5 disappears? We end up with three disconnected components:

  • Nodes 12, 3, and 4
  • Nodes 6, 7, and 8
  • Nodes 9, 10, and 11

This new layout is illustrated in the following diagram:

As you can see, communication from one component to another will be completely impossible. Consider, for instance, nodes 1 and 10. There is no possible path between them anymore. In a telecommunication or road network, this situation can have serious consequences, from huge traffic jams to the impossibility of calling emergency services. It needs to be avoided at any cost, meaning that nodes such as node 5 in our test graph need to be identified in order to be better protected. For this reason, node 5 is called a critical (or bridging) node.

Fortunately, we have centrality algorithms to measure this kind of importance. We will group them together under...

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