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)

Simulation and Analysis

Network structure is intimately related to the processes that occur in networked systems. Different processes create different structures, and different structures influence networked processes. Simulations can be used to generate synthetic networks. These networks can be used to study how structure forms in real systems. Real or synthetic networks can also be used to simulate processes that occur on those networks, to understand the influence of structure on those processes. This chapter introduces several common synthetic network models, as well as an example of simulating networked processes using agent-based modeling.

In this chapter, we will cover the following topics:

  • Watts-Strogatz networks: Simulating small worlds by adding random shortcuts to locally-clustered networks
  • Preferential attachment: How the rich getting richer creates scale-free heavy...