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)

Projections

While affiliation networks are useful for representing the full structure of many-to-many relationships, it is sometimes easier to work with standard single-mode networks. This might be the case if an analysis focuses on a particular type of node, or if a necessary technique is only available for single-mode networks, or if the affiliation network has too many nodes to visualize clearly. Luckily, it is possible to create single-mode networks out of an affiliation network using a process called projection. And, as you might expect, NetworkX makes it easy.

Single-mode networks built from affiliation networks are called co-affiliation networks, because the nodes are connected by an edge if they have common affiliations. There are several types of projections that are used to create co-affiliation networks, but they all revolve around the same idea: connecting nodes with...