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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

K-cores

In the previous section, we saw that cliques often form the cores of networks and communities, but that they are computationally difficult to find. In larger networks, k-cores can be a practical alternative for finding dense regions. A k-core is created by removing all nodes of degree less than k from a network. The number k can be anything you choose. The larger k is, the more nodes will be stripped away.

The nodes that remain in a k-core are highly connected to their neighbors. However, different parts of the network might become disconnected from each other after low-degree nodes are removed. The result is that a k-core consists of islands of highly connected nodes. These islands form the core of the network (hence the name k-core). Any remaining connections can be interpreted as highly-connected backbones that join different parts of the network.

NetworkX provides...

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