Social network analysis for startups
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...
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| Otros Autores: | |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Sebastopol :
O'Reilly
[2011]
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628022806719 |
Tabla de Contenidos:
- 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?
