Network-Science-with-Python-and-NetworkX-Quick-Start-Guide

Network Science with Python and NetworkX Quick Start Guide, published by Packt

This is the code repository for Network Science with Python and NetworkX Quick Start Guide, published by Packt.

Explore and visualize network data effectively

What is this book about?

NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use.

This book covers the following exciting features: <First 5 What you'll learn points>

• Learn how to manage users, groups, and permissions
• Encrypt and decrypt disks with Linux Unified Key Setup /Luks
• Setup SSH for remote access, and connect it to other nodes
• Understand how to add, remove, and search for packages
• Use NFS and Samba to share directories with other users

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

G_karate = nx.karate_club_graph()
mr_hi = 0
john_a = 33

Following is what you need for this book: Copy and paste the Audience section from the EPIC.

With the following software and hardware list you can run all code files present in the book (Chapter 1-12).

Software and Hardware List

Related products

• Python Machine Learning Cookbook - Second Edition [Packt] [Amazon]

• Python Deep Learning - Second Edition [Packt] [Amazon]

Get to Know the Author

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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