Title:
Social network analysis for startups / Maksim Tsvetovat and Alexander Kouznetsov. Social network analysis for startups : finding connections on the social web
Social network analysis for startups : finding connections on the social web Social network analysis for startups : finding connections on the social web
Author:
Tsvetovat, Maksim.
Kouznetsov, Alexander.
General Notes:
Subtitle from cover., Subtitle from cover.
Publisher:
O'Reilly Media,
Publication Place:
Sebastopol, CA :
ISBN:
9781449306465 (pbk.)
1449306462 (pbk.)
Subject:
Data mining.
Online social networks.
Contents:
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 Cells -- The Revolution Will Be Tweeted -- Graph Theory -- A Quick Introduction -- What Is a Graph? -- Adjacency Matrices -- Edge-Lists and Adjacency Lists -- 7 Bridges of Konigsberg -- Graph Traversals and Distances -- Depth-First Traversal -- Breadth-First Traversal -- Paths and Walks -- Dijkstra's Algorithm -- Graph Distance -- Graph Diameter -- Why This Matters -- 6 Degrees of Separation is a Myth! -- Small World Networks.
Centrality, Power, and Bottlenecks -- Sample 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" -- Find the Gossipmongers -- Find the Communication Bottlenecks and/or Community Bridges -- Putting It Together -- Who Is a "Gray Cardinal?" -- Klout Score -- Page Rank -- How Google Measures Centrality -- What Can't Centrality Metrics Tell Us? -- 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 Models -- Triads, Network Density, and Conflict -- 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 -- 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 -- How Does Information Shape Networks (and Vice Versa)? -- Birds of a Feather? -- Homophily vs. Curiosity -- Weak Ties -- Dunbar Number and Weak Ties -- A Simple Dynamic Model in Python -- Influences in the Midst.
Exercises for the Reader -- Coevolution of Networks and Information -- Exercises for the Reader -- Why Model Networks? -- Graph Data in the Real World -- Medium Data: The Tradition -- Big Data: The Future, Starting Today -- "Small Data" -- Flat File Representations -- EdgeList Files -- .net Format -- GML, GraphML, and other XML Formats -- Ancient Binary Format -- ##h Files -- "Medium Data": Database Representation -- What are Cursors? -- What are Transactions? -- Names -- Nodes as Data, Attributes as? -- The Class -- Functions and Decorators -- The Adaptor -- Working with 2-Mode Data -- Exercises for the Reader -- Social Networks and Big Data -- NoSQL -- Structural Realities -- Computational Complexities -- Big Data is Big -- Big Data at Work -- What Are We Distributing? -- Hadoop, S3, and MapReduce -- Hive -- SQL is Still Our Friend.
Physical Description:
xi, 174 pages : illustrations ;
Formatted Contents Note:
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Publication Date:
2011.
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