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)

K-cores

In the previous section, we saw that cliques often form the cores of networks and communities, but that they are computationally difficult to find. In larger networks, k-cores can be a practical alternative for finding dense regions. A k-core is created by removing all nodes of degree less than k from a network. The number k can be anything you choose. The larger k is, the more nodes will be stripped away.

The nodes that remain in a k-core are highly connected to their neighbors. However, different parts of the network might become disconnected from each other after low-degree nodes are removed. The result is that a k-core consists of islands of highly connected nodes. These islands form the core of the network (hence the name k-core). Any remaining connections can be interpreted as highly-connected backbones that join different parts of the network.

NetworkX provides...