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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

Diameter and mean shortest path

The size of a network can be quantified in several ways. In Chapter 5, The Small Scale – Nodes and Centrality, the distance between two nodes was defined as the length of the shortest path between them. With a way to measure distance, it becomes possible to define size based on that distance.

NetworkX provides several convenient functions for finding distances and shortest paths. The shortest paths between two particular nodes can be found using the all_shortest_paths() function:

list(nx.all_shortest_paths(G_karate, mr_hi, john_a))
[[0, 8, 33], [0, 13, 33], [0, 19, 33], [0, 31, 33]]

If you only need to know the distance, the shortest_path_length() function will provide that:

nx.shortest_path_length(G_karate, mr_hi, john_a)
2

On the other hand, the distance between all node pairs can be found using the shortest_path_length() function. This function...

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