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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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Estelle Scifo Estelle Scifo
Author Profile Icon Estelle Scifo
Estelle 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
Node Importance

In this chapter, we are going to talk about node importance, also known as centrality algorithms. As you will discover, several techniques have been developed, based on the definition of importance for a given graph and a given problem. We will learn about the most famous techniques, starting with degree centrality and the PageRank algorithm used by Google. For the latter, we will go through an example implementation and run it on a simple graph to fully understand how it works and when it can be used. After discovering the other types of centrality algorithms, such as betweenness centrality, we will conclude this chapter with explanations of how centrality algorithms can be used in the context of fraud detection. In this example, we will use, for the first time, the tools provided in the GDS to create a projected graph from Cypher in order to create fake relationships...

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