Chapter 1, What is a Network?, gives an overview of the history of network science and social network analysis, as well as introducing common types of networks and walking you through writing your first program with NetworkX.
Chapter 2, Working with Networks in NetworkX, describes simple, directed, and weighted networks, and how to work with them in NetworkX.
Chapter 3, From Data to Networks, describes functions for loading network data and for creating networks from scratch.
Chapter 4, Affiliation Networks, focuses on networks with two types of nodes (such as groups and group members) and shows how to work with these networks in NetworkX, as well as how to convert them to co-affiliation networks with just a single type of node.
Chapter 5, The Small Scale—Nodes and Centrality, shows how to use NetworkX to analyze network structure by looking at the properties of individual nodes and their connections.
Chapter 6, The Big Picture—Describing Networks, introduces several measures used to classify the structure of entire networks, and shows how these measures can differentiate between different types of real-world networks.
Chapter 7, In-Between—Communities, discusses medium-scale network structure, including community detection, clique detection, and k-cores.
Chapter 8, Social Networks and Going Viral, focuses on the special considerations that arise when network science is applied to social networks, as well as how the properties of social networks influence the spread of contagions such as disease or innovation.
Chapter 9, Simulation and Analysis, introduces several models used to generate networks based on underlying assumptions, as well as how to use agent-based models to simulate the evolution of a networked system.
Chapter 10, Networks in Space and Time, covers special considerations for network data associated with geographic locations and data that changes over time.
Chapter 11, Visualization, describes several visualization functions provided by NetworkX, as well as how to use them to visualize network information effectively.
Chapter 12, Conclusion, summarizes the lessons learned throughout this book, and provides resources for pursuing more advanced topics in network science.