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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

Steps to reproduce the Louvain algorithm

The Louvain algorithm aims at maximizing the modularity, which acts as the loss function. Starting from a graph where each node is assigned to its own community (Q=0), the algorithm will try to move nodes to the community of their neighbors, and keep this configuration only if it makes the modularity increase.

The algorithm performs two steps at each iteration. During the first one, an iteration over all nodes is performed. For each node n and each of its neighbors k, the algorithm tries to move the node n to the same community as k. The node n is moved to the community that leads to the highest increase in modularity. If it is not possible to increase the modularity with such an operation, the community of node n is left unchanged for this iteration.

In the second step, the algorithm will group together nodes belonging to the same community in order to create new nodes, and sum the weights of the inter-community edges in order to create the new...

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