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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 shown how networks that exist in time or space can be represented, analyzed, and visualized in NetworkX. Such networks have additional constraints imposed by the physical realities of time and space. Spatial networks can be visualized using the actual locations of nodes. Gravity models can be used to compensate for different lengths when comparing edge properties. Networks that change over time can be analyzed by creating snapshots, and can possibly link those snapshots into a layered network. This chapter gave examples of working with temporal and spatial networks using US air traffic data and historical data on Dutch Wikipedia articles. The next chapter will cover some advanced visualization techniques in NetworkX.

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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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