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 time

As we march forward toward the inevitable heat death of the universe, all things change, including networks. Networks that change in time are called dynamic networks. Network science is often concerned with how the structure of networks influences underlying systems. But, the reverse can be true as well: the processes that take place in a system can influence its network structure.

One approach to understanding how networks change over time is to look at snapshots. A snapshot is a network containing only the nodes and edges that were present at a specific point in time (like taking a picture). By taking snapshots at different times, network properties can be calculated at each point to understand how the network has evolved. One way to represent dynamic networks is to include all nodes and edges, but annotate them with the times them were present in the network...