Social network analysis for startups [e-book] / Maksim Tsvetovat and Alexander Kouznetsov.

Tsvetovat, Maksim.
1st edition
Sebastopol : O'Reilly, [2011]
1 online resource (190 p.)
Data mining.
Online social networks.
Electronic books.
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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, analyz
Table of Contents; Preface; Prerequisites; Open-Source Tools; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Content Updates; March 16, 2012; Thanks; Chapter 1. Introduction; Analyzing Relationships to Understand People and Groups; Binary and Valued Relationships; Symmetric and Asymmetric Relationships; Multimode Relationships; 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; Social Networks in Prison
Informal Networks in Terrorist CellsThe Revolution Will Be Tweeted; Social Media and Social Networks; Egyptian Revolution and Twitter; Chapter 2. Graph Theory-A Quick Introduction; What Is a Graph?; Adjacency Matrices; Edge-Lists and Adjacency Lists; 7 Bridges of Königsberg; Graph Traversals and Distances; Depth-First Traversal; Implementation; DFS with NetworkX; Breadth-First Traversal; Algorithm; BFS with NetworkX; Paths and Walks; Dijkstra's Algorithm; Graph Distance; Graph Diameter; Why This Matters; 6 Degrees of Separation is a Myth!; Small World Networks
Chapter 3. Centrality, Power, and BottlenecksSample Data: The Russians are Coming!; Get Oriented in Python and NetworkX; Read Nodes and Edges from LiveJournal; Snowball Sampling; Saving and Loading a Sample Dataset from a File; Centrality; Who Is More Important in this Network?; Find the "Celebrities"; Degree centrality in the LiveJournal network; Find the Gossipmongers; Find the Communication Bottlenecks and/or Community Bridges; Putting It Together; Who Is a "Gray Cardinal?"; In practice; Klout Score; PageRank-How Google Measures Centrality; Simplified PageRank algorithm
What Can't Centrality Metrics Tell Us?Chapter 4. Cliques, Clusters and Components; Components and Subgraphs; Analyzing Components with Python; Islands in the Net; Subgraphs-Ego Networks; Extracting and Visualizing Ego Networks with Python; Triads; Fraternity Study-Tie Stability and Triads; Triads and Terrorists; The "Forbidden Triad" and Structural Holes; Structural Holes and Boundary Spanning; Triads in Politics; Directed Triads; Analyzing Triads in Real Networks; Real Data; Cliques; Detecting Cliques; Hierarchical Clustering; The Algorithm; Clustering Cities; Preparing Data and Clustering
Block ModelsTriads, Network Density, and Conflict; Chapter 5. 2-Mode Networks; Does Campaign Finance Influence Elections?; Theory of 2-Mode Networks; Affiliation Networks; Attribute Networks; A Little Math; 2-Mode Networks in Practice; PAC Networks; Candidate Networks; Expanding Multimode Networks; Exercise; Chapter 6. Going Viral! Information Diffusion; Anatomy of a Viral Video; What Did Facebook Do Right?; How Do You Estimate Critical Mass?; Wikinomics of Critical Mass; Content is (Still) King; Heterogenous Preferences; How Does Information Shape Networks (and Vice Versa)?
Birds of a Feather?
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Description based on online resource; title from PDF title page (ebrary, viewed September 22, 2013).
Kouznetsov, Alexander.
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