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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

Who this book is for

I have tried to write this book for aspiring network scientists, managers who work with network scientists, and students from high school to Ph.D. level; anyone who wants to learn the basics of network science from the ground up. The only previous knowledge you will need is some familiarity with the Python programming language, or programming in general. I will avoid the mathematical details, instead focusing on the programming and applications. For the truly adventurous reader, the basic mathematics of networks are described in Appendix.

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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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