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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

Global clustering

The level of clustering or transitivity in a network can be quantified using triangles, just as the transitivity was quantified for individual nodes in Chapter 5, The Small Scale – Nodes and Centrality. These measures describe, overall, how common triangles are within a network.

The simplest measure of large-scale clustering is transitivity: the fraction of possible triangles that are present. The following example uses the transitivity() function to calculate this value for the example networks:

nx.transitivity(G_karate)
0.2556818181818182

nx.transitivity(G_electric)
0.07190412782956059

nx.transitivity(G_internet)
0.135678391959799

An alternative approach is to average the local clustering coefficient (described in Chapter 5, The Small Scale – Nodes and Centrality) over all nodes. This measure is sometimes called the global clustering coefficient. In...

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