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Hands-On Graph Analytics with Neo4j

You're reading from  Hands-On Graph Analytics with Neo4j

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781839212611
Pages 510 pages
Edition 1st Edition
Languages
Author (1):
Estelle Scifo Estelle Scifo
Profile icon Estelle Scifo

Table of Contents (18) Chapters

Preface 1. Section 1: Graph Modeling with Neo4j
2. Graph Databases 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

Understanding the Louvain algorithm

The Louvain algorithm was proposed in 2008 by researchers from the university of Louvain in Belgium, giving the algorithm its name. It relies on a measure of the density of connections within a community compared to the connections toward other nodes. This metric is called modularity, and it is the variable we are going to understand first.

Defining modularity

Modularity is a metric quantifying the density of links within nodes in the same community, compared to links between nodes in different communities.

Mathematically speaking, its definition is as follows:

Q = 1/(2m) * Σij [ Aij - kikj / (2m)] δ(ci, cj)

Where:

  • Aij is 1 if nodes i and j are connected, 0 otherwise.
  • ki is the degree of node i.
  • m is the sum of all weights carried by the edges. We also have the relation Σi ki = 2m, since the sum over all nodes will count each edge twice.
  • ci is the community the node i is assigned to by the algorithm.
  • δ(x, y) is the Kronecker...
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