Models and Methods in Social Network Analysis (Structural Analysis in the Social Sciences)

Cover......Page 1
Half-title......Page 3
Series-title......Page 5
Title......Page 7
Copyright......Page 8
Contents......Page 9
Acknowledgments......Page 11
Contributors......Page 13
1 Introduction......Page 17
References......Page 23
2.1 Network Study Designs......Page 24
2.2 Setting Network Boundaries......Page 25
2.3 Survey and Questionnaire Methods......Page 26
(A) Name Generator Instruments for Egocentric Networks......Page 27
(B) Additional Instruments for Egocentric Networks......Page 34
(D) Informant Biases in Network Perception......Page 36
2.4 Informant Accuracy and Competence......Page 37
(B) Studies of Informant Competence......Page 38
(C) Prospective Uses of Informants......Page 39
2.5 Archival Network Data......Page 40
2.6 Observation......Page 41
References......Page 42
3.1 Introduction......Page 47
3.2 Preliminaries on Survey Sampling......Page 48
3.3 Variables in Network Surveys......Page 51
3.4 Sample Selection in Network Surveys......Page 53
3.5 Probabilistic Network Models......Page 55
3.6 A Class of Network Models with Dyad Dependence......Page 57
3.7 Continuous Dyad Dependence Models......Page 59
3.8 Discrete Dyad Dependence Models......Page 61
3.9 Log-Linear Representations of Models with Dyad Dependence......Page 64
3.10 Clustered Versions of Models with Dyad Dependence......Page 66
3.11 Applications......Page 68
References......Page 69
4.1 Introduction......Page 73
4.2 Group Centrality......Page 74
(A) Degree......Page 75
(B) Closeness......Page 77
(C) Betweenness......Page 78
(A) Degree......Page 79
(B) Closeness and Betweenness......Page 81
(C) Centralization......Page 82
4.4 Core-Periphery Measures......Page 84
4.5 Conclusion......Page 89
References......Page 91
5.1 Introduction......Page 93
(A) Structural Equivalence......Page 95
(B) Regular Equivalence......Page 96
(C) Ideal Blocks and Measures for Blockmodels......Page 98
(E) A Clustering Algorithm......Page 99
5.2 The Generalized Blockmodeling Approach......Page 101
(A) Revisiting the Little League Network......Page 102
(A) A Baboon Network as a Center-Periphery Structure......Page 104
(B) Ranked-Clusters Models of Stratified Sociometric Systems......Page 106
5.4 Formalization of Blockmodeling......Page 109
(B )Criterion Functions......Page 110
5.5 Conclusion......Page 111
References......Page 112
6.1 Introduction......Page 114
(A) Macro Models......Page 115
(B) Spatial Autocorrelation......Page 116
(C) Network Models......Page 117
6.2 Empirical Studies......Page 120
(A) Event History Analysis......Page 124
6.3 Network-Based Interventions......Page 127
6.4 Conclusion......Page 128
Acknowledgments......Page 129
References......Page 130
7.1 Background......Page 133
7.2 Affiliation Networks......Page 134
7.5 Bipartite Graph......Page 136
7.6 Joint Representation of Actors and Events......Page 139
7.7 Matrix Decomposition......Page 140
7.8 Relationship Between Scores and Input Data......Page 141
7.9 Scores for Correspondence Analysis......Page 142
7.11 Chi-Square Distance Interpretations......Page 144
7.12 Inertia......Page 146
7.14 Between-Set Comparisons......Page 147
7.15 Symmetric Representation......Page 148
7.16 Comparing Solutions......Page 151
7.17 Distances Versus Dimensions......Page 153
7.18 Correspondence Analysis of Western Hemisphere Countries......Page 155
7.19 Conclusion......Page 159
Appendix: List of Western Hemisphere Organizations......Page 163
8.1 Some Notation......Page 164
(B) Applications......Page 166
8.3 p......Page 168
(A) Estimation......Page 169
(B) Pseudolikelihood Estimation......Page 170
(C) Maximum Likelihood Estimation: Simulating  p* Models and Model Degeneracy......Page 171
(D) Comparing Pseudolikelihood and Monte Carlo Maximum Likelihood Estimation......Page 173
References......Page 175
9.1 Introduction......Page 178
9.2 Notation and Basic Terminology......Page 179
9.3 Random Graph Models for Multiple Relations......Page 180
(A) Assumptions......Page 181
(B) Dependence Graphs......Page 182
(C) The Random Multigraph Model......Page 183
(D) Estimation......Page 185
9.5 Example: Krackhardt’s High-Tech Managers......Page 187
9.6 Multiple Rater Networks/Cognitive Social Structures......Page 193
(A) Notation and Terminology......Page 194
(A) Dependence Graphs......Page 195
(B) Multiple Rater Random Graph Model......Page 197
(C) Example: Krackhardt’s High-Tech Managers......Page 198
References......Page 203
10.1 Social Phenomena, Networks, and Dependence......Page 208
(A) Social Networks and Dependence Graphs......Page 210
10.2 Some Aspects of Graphical Modeling......Page 211
(B) Factorization and the Hammersley-Clifford Theorem......Page 212
(B) Dyadic Dependence Structures......Page 214
(C) Markov Random Graphs......Page 215
(A) Setting Structures......Page 218
(B) Partial Dependence Structures......Page 219
(A) Two-Block Chain Graph......Page 221
(B) Social Influence Models......Page 222
(C) Social Selection Models......Page 225
(D) Temporal Models......Page 226
10.6 Social Space: An Interpretation of Social Dependence......Page 227
Endnotes......Page 228
References......Page 229
11.1 Some Basic Ideas About Longitudinal Social Network Data......Page 231
11.3 Example......Page 233
11.4 Continuous-Time Markov Chains......Page 234
11.5 A Simple Model: Independent Arcs......Page 237
11.6 The Reciprocity Model......Page 238
11.7 The Popularity Model......Page 239
(A) Rate Function......Page 240
(B) Objective Function......Page 241
(C) Gratification Function......Page 242
11.9 Specification of the Actor-Oriented Model......Page 243
(A) Objective Function......Page 244
(B) Rate Function......Page 248
(A) Method of Moments......Page 250
(B) Robbins-Monro Procedure......Page 252
(C) Missing Data......Page 253
11.12 Actor-Oriented Model Results for the Example......Page 254
(B) Density Parameter......Page 257
(C) Other Parameters......Page 259
11.14 Discussion......Page 260
References......Page 262
12.1 Visual Images......Page 264
12.2 The Search for Structure......Page 267
12.3 Finding Correlates of Structural Patterns......Page 270
12.4 Exploratory Research......Page 272
12.5 Examining an a priori Hunch......Page 276
12.6 Validating a Model......Page 280
12.7 A Post hoc Analysis......Page 281
12.8 Conclusion......Page 283
References......Page 284
13.1 Introduction......Page 286
13.2 Social Network Software – A Closer Look......Page 290
(B) UCINET......Page 291
(C) Pajek......Page 296
(D) NetMiner II......Page 302
(E) STRUCTURE......Page 308
(F) MultiNet......Page 312
(G) StOCNET......Page 317
(A) General Packages......Page 321
(B) Special Purpose Packages......Page 323
(C) Utilities and Routines......Page 325
13.4 Recommendations......Page 326
Acknowledgments......Page 328
References......Page 329
Index......Page 333