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)

Adding edge weights

So far, all of the edges in this chapter have been unweighted, but the Graph class also supports weighted edges. Edge weights are handy when connections can have different strengths and when there is a way to quantify the strength of a connection; for example, how often two friends talk to each other, the volume of fluid a pipe can transport, or the number of direct flights between two cities.

The karate club network doesn't have any additional information about the strength of the edges, but there are relevant properties of those edges that can be calculated, such as the tie strength. Tie strength increases with the number of neighbors that two nodes have in common. It is motivated by the observation that closer friends tend to have more friends in common, and it can often reveal insight into the structure of a social network. The following code calculates...