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). Thus, we may arrive at results that may seem counter-intuitive -- e.g. that Jusin Bieber (7.5 mil. followers) and Lady Gaga (7.2 mil. followers) have relatively little actual influence despite their celebrity status -- while a middle-of-the-road blogger with 30K followers is able to generate tweets that "go viral" and result in millions of impressions. O'Reilly's "Mining Social Media" and "Programming Collective Intelligence" books are an excellent start for people inteseted in SNA. This book builds on these books' foundations to teach a new, pragmatic, way of doing SNA. I would like to write a book that links theory ("why is this important?", "how do various concepts interact?", "how do I interpret quantitative results?") and practice -- gathering, analyzing and visualizing data using Python and other open-source tools.
Table of Contents
- Social Network Analysis for Startups
- A Note Regarding Supplemental Files
- Analyzing Relationships to Understand People and Groups
- From Relationships to Networks—More Than Meets the Eye
- Social Networks vs. Link Analysis
- The Power of Informal Networks
- Terrorists and Revolutionaries: The Power of Social Networks
2. Graph Theory—A Quick Introduction
- What Is a Graph?
- Graph Traversals and Distances
- Graph Distance
- Why This Matters
- 6 Degrees of Separation is a Myth!
- Small World Networks
3. Centrality, Power, and Bottlenecks
- Sample Data: The Russians are Coming!
- Who Is More Important in this Network?
- Find the “Celebrities”
- Find the Gossipmongers
- Find the Communication Bottlenecks and/or Community Bridges
- Putting It Together
- Who Is a “Gray Cardinal?”
- Klout Score
- PageRank—How Google Measures Centrality
- What Can’t Centrality Metrics Tell Us?
4. Cliques, Clusters and Components
- Components and Subgraphs
- Subgraphs—Ego Networks
- Hierarchical Clustering
- Triads, Network Density, and Conflict
- 5. 2-Mode Networks
6. Going Viral! Information Diffusion
- Anatomy of a Viral Video
- How Does Information Shape Networks (and Vice Versa)?
- A Simple Dynamic Model in Python
- Coevolution of Networks and Information
7. Graph Data in the Real World
- Medium Data: The Tradition
- Big Data: The Future, Starting Today
- “Small Data”—Flat File Representations
- “Medium Data”: Database Representation
- Working with 2-Mode Data
- Social Networks and Big Data
- Big Data at Work
- A. Data Collection
- B. Installing Software
- About the Authors