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)

From Data to Networks

To analyze a system using NetworkX, that system must first be modeled as a network, and then be represented as an object within NetworkX. This chapter explains the basic process of creating network representations of data. The first section covers the part of the process that takes place in your head: modeling data as a network. The remaining sections demonstrate the part of the process that happens in code: creating a NetworkX Graph from data, using two different methods. In the first method, data is reformatted into one of the standard network formats supported by NetworkX. In the second method, for more complex data, a network is created from scratch, by using code to add nodes and edges one at a time.

In this chapter, we will cover the following topics:

  • Modeling data: Giving meaning to nodes and edges
  • Network files: Saving your networks to files
  • Networks...