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)

Networks in Space and Time

Throughout this book so far, nodes have existed outside of any particular time or space. In visualizations, nodes have been placed on a two-dimensional space (pages), but only because it would be difficult to tell them apart if they were all on top of each other. In some cases, nodes truly are associated with a particular spatial location or a particular time. In these cases, additional techniques can be helpful for visualizing and analyzing networks. This chapter describes some of the special techniques needed for networks in space and time and applies them to real-world examples, including airports and links on Wikipedia.

Topics in this chapter include the following:

  • Locations and events: Explaining how networks can be used to represent data with temporal and spatial properties
  • Networks in space: Visualizing and analyzing relationships between locations...