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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? 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

Adding edge weights

So far, all of the edges in this chapter have been unweighted, but the Graph class also supports weighted edges. Edge weights are handy when connections can have different strengths and when there is a way to quantify the strength of a connection; for example, how often two friends talk to each other, the volume of fluid a pipe can transport, or the number of direct flights between two cities.

The karate club network doesn't have any additional information about the strength of the edges, but there are relevant properties of those edges that can be calculated, such as the tie strength. Tie strength increases with the number of neighbors that two nodes have in common. It is motivated by the observation that closer friends tend to have more friends in common, and it can often reveal insight into the structure of a social network. The following code calculates...

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