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)

Appendix

The branch of mathematics studying networks is called graph theory. Graph and network are more or less two words for the same thing, but mathematicians can be picky about exact definitions. A graph is composed of two parts: a set of things called vertices and a set of edges representing connections between those vertices.

What is a vertex? It's a mathematical object whose sole purpose is to be connected to other vertices. In other words, it's pretty much the same thing as a node. In order to tell vertices apart, it is necessary to give them some kind of label. These labels could be anything, but let's call them v1, v2, and so on. It's a common convention to call a set of vertices V. Mathematically, this can be written using the following set notation, where N is the number of vertices in V:

V = {v1, v2, ..., vN},

Connections between vertices are called...