000			04723nam a2200301 a 4500
003			AR-sfUTN
008			170717b ||||| |||| 00| 0 d
020			_a9781449306465
040			_cAR-sfUTN
041			_aeng
080			_a004.773 T789 _22000
100	1		_aTsvetovat, Maksim _917325
245	1	0	_aSocial network analysis for startups / _cMaksim Tsvetovat and Alexander Kouznetsov.
260			_aSebastopol, CA: _bO'Reilly, _c2011
300			_a174 p.
336			_2rdacontent _atexto _btxt
337			_2rdamedia _asin mediación _bn
338			_2rdacarrier _avolumen _bnc
505	8	0	_aCONTENIDO 1. Introduction 1 Analyzing Relationships to Understand People and Groups 2 Binary and Valued Relationships 2 Symmetric and Asymmetric Relationships 3 Multimode Relationships 3 From Relationships to Networks - More Than Meets the Eye 3 Social Networks vs. Link Analysis 4 The Power of Informal Networks 6 Terrorists and Revolutionaries: The Power of Social Networks 10 Social Networks in Prison 10 Informal Networks in Terrorist Cells 11 The Revolution Will Be Tweeted 14 2. Graph Theory - A Quick Introduction 19 What Is a Graph? 19 Adjacency Matrices 21 Edge-Lists and Adjacency Lists 22 7 Bridges of Konigsberg 23 Graph Traversals and Distances 25 Depth-First Traversal 27 Breadth-First Traversal 30 Paths and Walks 31 Dijkstra's Algorithm 33 Graph Distance 35 Graph Diameter 36 Why This Matters 36 6 Degrees of Separation is a Myth! 37 Small World Networks 37 3. Centrality, Power, and Bottlenecks 39 Sample Data: The Russians are Coming! 39 Get Oriented in Python and NetworkX 39 Read Nodes and Edges from LiveJournal 41 Snowball Sampling 43 Saving and Loading a Sample Dataset from a File 44 Centrality 45 Who Is More Important in this Network? 45 Find the "Celebrities" 45 Find the Gossipmongers 49 Find the Communication Bottlenecks and/or Community Bridges 51 Putting It Together 54 Who Is a "Gray Cardinal"? 55 Klout Score 57 Page Rank - How Google Measures Centrality 58 What Can't Centrality Metrics Tell Us? 60 4. Cliques, Clusters and Components 61 Components and Subgraphs 61 Analyzing Components with Python 62 Islands in the Net 62 Subgraphs - Ego Networks 65 Extracting and Visualizing Ego Networks with Python 65 Triads 67 Fraternity Study - Tie Stability and Triads 68 Triads and Terrorists 68 The "Forbidden Triad" and Structural Holes 72 Structural Holes and Boundary Spanning 73 Triads in Politics 74 Directed Triads 76 Analyzing Triads in Real Networks 77 Real Data 79 Cliques 79 Detecting Cliques 79 Hierarchical Clustering 81 The Algorithm 82 Clustering Cities 83 Preparing Data and Clustering 84 Block Models 86 Triads, Network Density, and Conflict 88 5. 2-Mode Networks 93 Does Campaign Finance Influence Elections? 93 Theory of 2-Mode Networks 96 Affiliation Networks 96 Attribute Networks 98 A Little Math 98 2-Mode Networks in Practice 100 PAC Networks 102 Candidate Networks 102 Expanding Multimode Networks 105 Exercise 107 6. Going Viral! Information Diffusion 109 Anatomy of a Viral Video 109 What Did Facebook Do Right? 110 How Do You Estimate Critical Mass? 111 Wikinomics of Critical Mass 112 Content is (Still) King 113 How Does Information Shape Networks (and Vice Versa)? 116 Birds of a Feather? 117 Homophily vs. Curiosity 117 Weak Ties 119 Dunbar Number and Weak Ties 119 A Simple Dynamic Model in Python 121 Influences in the Midst 125 Exercises for the Reader 127 Coevolution of Networks and Information 127 Exercises for the Reader 133 Why Model Networks? 134 7. Graph Data in the Real World 137 Medium Data: The Tradition 138 Big Data: The Future, Starting Today 138 "Small Data" - Flat File Representations 139 EdgeList Files 139 .net Format 140 GML, GraphML, and other XML Formats 141 Ancient Binary Format - ##h Files 142 "Medium Data": Database Representation 142 What are Cursors? 143 What are Transactions? 144 Names 144 Nodes as Data, Attributes as? 145 The Class 145 Functions and Decorators 146 The Adaptor 148 Working with 2-Mode Data 150 Exercises for the Reader 151 Social Networks and Big Data 151 NoSQL 152 Structural Realities 153 Computational Complexities 156 Big Data is Big 156 Big Data at Work 156 What Are We Distributing? 157 Hadoop, S3, and MapReduce 157 Hive 158 SQL is Still Our Friend 160 A. Data Collection 161 B. Installing Software 171
650			_aSOCIAL NETWORKS
650			_aSOCIAL WEB
650			_aREDES SOCIALES
650			_aWEB SOCIAL
650			_aAPLICACIONES WEB
700	1		_aKouznetsov, Alexander
942			_cBK _2udc
999			_c13225 _d13225