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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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Toc

Table of Contents (15) Chapters Close

Preface 1. What is a Network? FREE CHAPTER 2. Working with Networks in NetworkX 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

The shell layout

If you liked the circle layout, you'll love the shell layout—it's just a lot of circles. The shell layout places nodes in concentric circles. Its benefits include the following:

  • Can visualize more nodes than a circular layout in the same space
  • More central nodes can be placed closer to the center to convey centrality information

However, the shell layout still does not capture community structure well, and can obscure some edges.

The following code uses the NetworkX shell_layout() function to visualize the karate club network. It's possible to use the default settings, but this example also uses community detection to place related nodes in similar locations:

degrees = dict(G.degree())
labels = sorted(degrees.keys(), key=lambda x: degrees[x], reverse=True)
nlist = []
i, k = 0, 6
while i < len(labels):
shell_labels = labels[i:i+k]
ordered_labels...
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