Preface;
Prerequisites;
Open-Source Tools;
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Content Updates;
Thanks;
Chapter 1: Introduction;
1.1 Analyzing Relationships to Understand People and Groups;
1.2 From Relationships to Networks-More Than Meets the Eye;
1.3 Social Networks vs. Link Analysis;
1.4 The Power of Informal Networks;
1.5 Terrorists and Revolutionaries: The Power of Social Networks;
Chapter 2: Graph Theory-A Quick Introduction;
2.1 What Is a Graph?;
2.2 Graph Traversals and Distances;
2.3 Graph Distance;
2.4 Why This Matters;
2.5 6 Degrees of Separation is a Myth!;
2.6 Small World Networks;
Chapter 3: Centrality, Power, and Bottlenecks;
3.1 Sample Data: The Russians are Coming!;
3.2 Centrality;
3.3 What Can't Centrality Metrics Tell Us?;
Chapter 4: Cliques, Clusters and Components;
4.1 Components and Subgraphs;
4.2 Subgraphs-Ego Networks;
4.3 Triads;
4.4 Cliques;
4.5 Hierarchical Clustering;
4.6 Triads, Network Density, and Conflict;
Chapter 5: 2-Mode Networks;
5.1 Does Campaign Finance Influence Elections?;
5.2 Theory of 2-Mode Networks;
5.3 Expanding Multimode Networks;
Chapter 6: Going Viral! Information Diffusion;
6.1 Anatomy of a Viral Video;
6.2 How Does Information Shape Networks (and Vice Versa)?;
6.3 A Simple Dynamic Model in Python;
6.4 Coevolution of Networks and Information;
Chapter 7: Graph Data in the Real World;
7.1 Medium Data: The Tradition;
7.2 Big Data: The Future, Starting Today;
7.3 "Small Data"-Flat File Representations;
7.4 "Medium Data": Database Representation;
7.5 Working with 2-Mode Data;
7.6 Social Networks and Big Data;
7.7 Big Data at Work;
Data Collection;
A Note on the Ethics of Data Collection;
The Old-Fashioned Way;
Mining Server Logs;
Mining Social Media Sites;
Twitter Data Collection;
Facebook;
Installing Software;
Why (We Love) Python?;
Exploratory Programming;
Python;
IPython;
NetworkX;
matplotlib;