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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

Watts-Strogatz and small worlds

The small-world problem (discussed in Chapter 8, Social Networks and Going Viral) asks how it is possible for distant people to be connected by short paths, even when everyone's connections are local (Travers & Milgram, 1967). Duncan Watts and Steven Strogatz (1998) developed a class of networks to explain this behavior. The networks begin as k-rings: nodes placed around a circle, with each node connected to its nearest k neighbors. Then, with probability p, each node's edges are moved to a randomly selected other node. These rewirings create shortcuts across the network. Even a small number of shortcuts greatly reduces the distances between nodes in the network, resolving the small-world problem.

The following code uses the NetworkX function watts_strogatz_graph() to generate Watts-Strogatz small-world networks with p=0, p=0.1, and...

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