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Does your startup rely on social network analysis? This concise guide provides a statistical framework to help you identify social processes hidden among the tons of data now available.
Social network analysis (SNA) is a discipline that predates Facebook and Twitter by 30 years. Through expert SNA researchers, you'll learn concepts and techniques for recognizing patterns in social media, political groups, companies, cultural trends, and interpersonal networks. You'll also learn how to use Python and other open source tools—such as NetworkX, NumPy, and Matplotlib—to gather, analyze, and visualize social data. This book is the perfect marriage between social network theory and practice, and a valuable source of insight and ideas.
* Discover how internal social networks affect a company’s ability to perform
* Follow terrorists and revolutionaries through the 1998 Khobar Towers bombing, the 9/11 attacks, and the Egyptian uprising
* Learn how a single special-interest group can control the outcome of a national election
* Examine relationships between companies through investment networks and shared boards of directors
* Delve into the anatomy of cultural fads and trends—offline phenomena often mediated by Twitter and Facebook
List of contents
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;
About the author
Maksim Tsvetovat is an interdisciplinary scientist, a software engineer, and a jazz musician. He has received his doctorate from Carnegie Mellon University in the field of Computation, Organizations and Society, concentrating on computational modeling of evolution of social networks, diffusion of information and attitudes, and emergence of collective intelligence. Currently, he teaches social network analysis at George Mason University. He is also a co-founder of DeepMile Networks, a startup company concentrating on mapping influence in social media. Maksim also teaches executive seminars in social network analysis, including "Social Networks for Startups" and"Understanding Social Media for Decisionmakers".
Alex Kouznetsov is an open-source software developer. He has developed a number of social network analysis tools for the industry, from large-scale data collection to online analysis and presentation tools.
Summary
SNA techniques are derived from sociological and social-psychological theories and take into account the whole network (or, in case of very large networks such as Twitter -- a large segment of the network).
