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Network Science with Python and NetworkX Quick Start Guide

Network Science with Python and NetworkX Quick Start Guide

By : Platt
5 (3)
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Network Science with Python and NetworkX Quick Start Guide

Network Science with Python and NetworkX Quick Start Guide

5 (3)
By: 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)
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Cliques

In the densest neighborhoods of a network, it is sometimes possible to find groups of nodes that are all connected to each other. Such groups are called cliques. Identification of cliques is another way to analyze the medium-scale structure of a network. Because cliques are highly interconnected, the nodes in a clique rarely belong to different communities. In fact, cliques often form the cores of communities.

Chapter 4, Affiliation Networks, described how to transform an affiliation network into a single-mode network using projections. For each node removed by such projections, its neighbors are connected into a clique. Cliques can give clues to an underlying affiliation network structure.

NetworkX provides the find_cliques() function to find cliques in a network. This function returns an iterator over all cliques in an arbitrary order. For example, using this function...

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