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Network Science with Python and NetworkX Quick Start Guide

You're reading from   Network Science with Python and NetworkX Quick Start Guide Explore and visualize network data effectively

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789955316
Length 190 pages
Edition 1st Edition
Languages
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Author (1):
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Edward L. Platt Edward L. Platt
Author Profile Icon Edward L. Platt
Edward L. Platt
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Table of Contents (15) Chapters Close

Preface 1. What is a Network? 2. Working with Networks in NetworkX FREE CHAPTER 3. From Data to Networks 4. Affiliation Networks 5. The Small Scale - Nodes and Centrality 6. The Big Picture - Describing Networks 7. In-Between - Communities 8. Social Networks and Going Viral 9. Simulation and Analysis 10. Networks in Space and Time 11. Visualization 12. Conclusion 13. Other Books You May Enjoy Appendix

Community detection in NetworkX

NetworkX provides several community detection algorithms. There are many ways to define communities, and many ways to search for them. However, in general, the NetworkX community detection functions take a Graph object and return a list or iterator over communities, represented as a set of node IDs.

The simplest type of community detection searches for non-overlapping communities. In other words, a network is partitioned into communities such that each node belongs to exactly one community.

Modularity maximization

One approach to finding such a partition is to define a function that quantifies the quality of a set of partitions, and then adjust the partition to maximize the quality. One popular...

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