Book Image

Network Science with Python and NetworkX Quick Start Guide

By : Edward L. Platt
Book Image

Network Science with Python and NetworkX Quick Start Guide

By: Edward L. Platt

Overview of this book

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. If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts. By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems.
Table of Contents (15 chapters)

Measuring resilience

Resilience is the ability of a system to withstand errors and attacks. In an electrical grid, for example, resilience would mean keeping power flowing when a transmission line or generator broke down. In traffic, it could mean the ability to reroute cars when a street is closed due to an accident.

Resilience is fundamentally a network property because it is usually achieved with redundant paths. When one path is no longer available, the others can still be used.

The simplest (and crudest) measure of resilience is the density of a network: the fraction of possible edges that exist. The more edges present in a network, the more redundant paths exist between its nodes. The following code uses the density() function to calculate this value for the example networks:

nx.density(G_karate)
0.13903743315508021

nx.density(G_karate)
0.011368341803124411

nx.density(G_karate...