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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

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781789955316

Length 190 pages

Edition 1st Edition

Languages

Python

Tools

NetworkX

Concepts

Data Science

Author (1):

Edward L. Platt

View More author details

Table of Contents (15) Chapters

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

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Appendix

Summary

This chapter has described many different techniques for quantifying the large-scale structure of networks. Network size can be quantified using the diameter or mean shortest path. Global clustering can be used to quantify how likely a node's neighbors are to be neighbors with each other. Connectivity measures, such as the minimum or average node/edge connectivity, are calculated by finding minimum cuts, and quantify network resilience. The chapter concluded by showing how inequality measures such as entropy and the Gini index can be used to turn small-scale centrality measures into large-scale measures of network centralization. The next chapter discusses medium-scale network structures and community detection.

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Authors (1)

Platt

Platt

Edward L. Platt creates technology for communities and communities for technology. He is currently a researcher at the University of Michigan School of Information and the Center for the Study of Complex Systems. He has published research on large-scale collective action, social networks, and online communities. He was formerly a staff researcher at the MIT Center for Civic Media. He contributes to many free/open source software projects, including tools for media analysis, network science, and cooperative organizations. He has also done research on quantum computing and fault tolerance. He has an M.Math in Applied Mathematics from the University of Waterloo, as well as B.S degrees in both Computer Science and Physics from MIT.

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