Exponential random graph models for social networks: theories, methods, and applications
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge University Press
2013
|
| Edition: | 1. publ. |
| Series: | Structural analysis in the social sciences
35 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XXII, 336 S. Ill., graph. Darst. 23 cm |
| ISBN: | 9780521141383 9780521193566 |
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| 245 | 1 | 0 | |a Exponential random graph models for social networks |b theories, methods, and applications |c ed. by Dean Lusher ... |
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| adam_text | Titel: Exponential random graph models for social networks
Autor: Lusher, Dean
Jahr: 2013
Contents
List of Figures page xvii
List of Tables xxi
1. Introduction 1
1.1 Intent of This Book 2
1.2 Software and Data 2
1.3 Structure of the Book 3
1.3.1 Section I: Rationale 3
1.3.2 Section II: Methods 3
1.3.3 Section III: Applications 4
1.3.4 Section IV: Future 5
1.4 How To Read This Book 5
1.5 Assumed Knowledge of Social Network Analysis 6
SECTION I: RATIONALE
2. What Are Exponential Random Graph Models? 9
2.1 Exponential Random Graph Models: A Short
Definition 9
2.2 ERGM Theory 10
2.3 Brief History of ERGMs 12
2.4 Network Data Amenable to ERGMs 14
3. Formation of Social Network Structure 16
3.1 Tie Formation: Emergence of Structure 16
3.1.1 Formation of Social Ties 16
3.1.2 Network Configurations: Consequential
Network Patterns and Related Processes 17
3.1.3 Local Network Processes 19
3.1.4 Dependency (and Theories of Network
Dependence) 19
3.1.5 Complex Combination of Multiple and Nested
Social Processes 21
3.2 Framework for Explanations of Tie Formation 23
3.2.1 Network Self-Organization 23
3.2.2 Individual Attributes 26
3.2.3 Exogenous Contextual Factors: Dyadic
Covariates 28
4. Simplified Account of an Exponential Random Graph Model
as a Statistical Model 29
4.1 Random Graphs 30
4.2 Distributions of Graphs 31
4.3 Some Basic Ideas about Statistical Modeling 34
4.4 Homogeneity 35
5. Example Exponential Random Graph Model Analysis 37
5.1 Applied ERGM Example: Communication in The
Corporation 37
5.2 ERGM Model and Interpretation 41
5.2.1 Multiple Explanations for Network Structure 45
SECTION II. METHODS
6. Exponential Random Graph Model Fundamentals 49
6.1 Chapter Outline 49
6.2 Network Tie-Variables 49
6.3 Notion of Independence 51
6.4 ERGMs from Generalized Linear Model Perspective 52
6.5 Possible Forms of Dependence 56
6.5.1 Bernoulli Assumption 56
6.5.2 Dyad-Independent Assumption 56
6.5.3 Markov Dependence Assumption 57
6.5.4 Realization-Dependent Models 57
6.6 Different Classes of Model Specifications 58
6.6.1 Bernoulli Model 58
6.6.2 Dyadic Independence Models 59
6.6.3 Markov Model 60
6.6.4 Social Circuit Models 69
6.7 Other Model Specifications 75
6.8 Conclusion 76
7. Dependence Graphs and Sufficient Statistics 77
7.1 Chapter Outline 77
7.2 Dependence Graph 78
7.2.1 Hammersley-Clifford Theorem and Sufficient
Statistics 82
7.2.2 Sufficient Subgraphs for Nondirected Graphs 83
7.3 Dependence Graphs Involving Attributes 88
7.4 Conclusion 89
8. Social Selection, Dyadic Covariates, and Geospatial Effects 91
8.1 Individual, Dyadic, and Other Attributes 91
8.2 ERGM Social Selection Models 93
8.2.1 Models for Undirected Networks 95
8.2.2 Models for Directed Networks 96
8.2.3 Conditional Odds Ratios 97
8.3 Dyadic Covariates 98
8.4 Geospatial Effects 99
8.5 Conclusion 101
9. Autologistic Actor Attribute Models 102
9.1 Social Influence Models 102
9.2 Extending ERGMs to Distribution of Actor Attributes 103
9.3 Possible Forms of Dependence 106
9.3.1 Independent Attribute Assumption 106
9.3.2 Network-Dependent Assumptions 107
9.3.3 Network-Attribute-Dependent Assumptions 107
9.3.4 Covariate-Dependent Assumptions 108
9.4 Different Model Specifications and Their
Interpretation 109
9.4.1 Independence Models 109
9.4.2 Network Position Effects Models 109
9.4.3 Network-Attribute Effects Models 111
9.4.4 Covariate Effects Models 112
9.5 Conclusion 113
10. Exponential Random Graph Model Extensions: Models for
Multiple Networks and Bipartite Networks 115
10.1 Multiple Networks 115
10.1.1 ERGMs for Analyzing Two Networks 116
10.1.2 ERGM Specifications for Two Networks 116
10.2 Bipartite Networks 120
10.2.1 Bipartite Network Representation and Special
Features 121
10.2.2 ERGM Specifications for Bipartite Networks 122
10.2.3 Additional Issues for Bipartite Networks 128
11. Longitudinal Models 130
11.1 Network Dynamics 130
11.2 Data Structure 130
11.3 Model 131
11.3.1 Continuous-Time Markov Chain 131
11.3.2 Tie-Oriented Dynamics 132
11.3.3 Definition of Dynamic Process 133
11.3.4 Stationary Distribution 134
11.3.5 Estimation Based on Changes 135
11.3.6 Configurations for Networks 136
11.4 Relations to Other Models 137
11.4.1 Reciprocity Model as Precursor 137
11.4.2 Stochastic Actor-Oriented Models as
Alternatives 138
11.5 Conclusion 139
12. Simulation, Estimation, and Goodness of Fit 141
12.1 Exploring and Relating Model to Data in Practice 141
12.2 Simulation: Obtaining Distribution of Graphs for a
Given ERGM 142
12.2.1 Sampling Graphs Using Markov Chain Monte
Carlo 142
12.2.2 Metropolis Algorithm 146
12.3 Estimation 147
12.3.1 Maximum Likelihood Principle 147
12.3.2 Curved ERGMs 147
12.3.3 Bayesian Inference 148
12.4 Solving the Likelihood Equation 149
12.4.1 Importance Sampling: Geyer-Thompson
Approach 149
12.4.2 Stochastic Approximation: Robbins-Monro
Algorithm 151
12.4.3 Modifications for Longitudinal Model 154
12.5 Testing Effects 156
12.5.1 Approximate Wald Test 157
12.5.2 Alternative Tests 158
12.5.3 Evaluating Log-Likelihood 160
12.6 Degeneracy and Near-Degeneracy 160
12.7 Missing or Partially Observed Data 162
12.8 Conditional Estimation from Snowball Samples 163
12.9 Goodness of Fit 165
12.9.1 Approximate Bayesian GOF 166
13. Illustrations: Simulation, Estimation, and Goodness of Fit 167
13.1 Simulation 167
13.1.1 Triangulation 168
13.1.2 Degrees 171
13.1.3 Stars and Triangles Together 172
13.2 Estimation and Model Specification 174
13.2.1 Some Example Model Specifications 176
13.3 GOF 179
13.3.1 How Do You Know Whether You Have a
Good Model? 179
13.3.2 What If Your Model Does Not Fit a Graph
Feature? 184
13.3.3 Should a Model Explain Everything? 184
SECTION III. APPLICATIONS
14. Personal Attitudes, Perceived Attitudes, and Social
Structures: A Social Selection Model 189
14.1 Perceptions of Others and Social Behavior 189
14.2 Data and Measures 191
14.2.1 Social Network Questions 191
14.2.2 Attribute Measures 192
14.2.3 Analyses 193
14.2.4 Goodness of Fit 193
14.3 Model Specification 194
14.3.1 Purely Structural Effects 194
14.3.2 Actor-Relation Effects 194
14.3.3 Covariate Network Effects 194
14.4 Results 195
14.4.1 Example 1: Schoolboys 196
14.4.2 Example 2: Football Team 199
14.5 Discussion 200
15. How To Close a Hole: Exploring Alternative Closure
Mechanisms in Interorganizational Networks 202
15.1 Mechanisms of Network Closure 202
15.2 Data and Measures 205
15.2.1 Setting and Data 205
15.3 Model Specification 207
15.4 Results 208
15.5 Discussion 210
16. Interdependencies between Working Relations: Multivariate
ERGMs for Advice and Satisfaction 213
16.1 Multirelational Networks in Organizations 213
16.2 Data, Measures, and Analyses 215
16.3 Descriptive Results 216
16.4 Multivariate ERGM Results 219
16.4.1 Low-AS Bank 219
16.4.2 High-AS Bank 222
16.5 Discussion 224
17. Brain, Brawn, or Optimism? Structure and Correlates of
Emergent Military Leadership 226
17.1 Emergent Leadership in Military Context 226
17.1.1 Antecedents to Emergent Leadership 226
17.1.2 Structure of Emergent Leadership 228
17.1.3 Setting and Participants 229
17.2 Model Specification 231
17.2.1 Modeling Issues 231
17.2.2 Purely Structural Effects 231
17.2.3 Actor-Relation Effects 232
17.3 Results 232
17.3.1 Results for Purely Structural Effects 232
17.3.2 Results for Actor-Relation Effects 234
17.4 Dicussion 235
18. Autologistic Actor Attribute Model Analysis of
Unemployment: Dual Importance of Who You Know and
Where You Live 237
18.1 Unemployment: Location and Connections 237
18.2 Data, Analysis, and Estimation 239
18.2.1 Data 239
18.2.2 Analysis 242
18.2.3 Estimation 243
18.3 Results 244
18.4 Discussion 246
19. Longitudinal Changes in Face-to-Face and Text
Message-Mediated Friendship Networks 248
19.1 Evolution of Friendship Networks, Communication
Media, and Psychological Dispositions 248
19.2 Data and Measures 251
19.2.1 Social Network Questions 251
19.2.2 Actor-Relation Measures 251
19.2.3 Analyses 252
19.3 Model Specification 252
19.4 Results 252
19.4.1 Results for Face-to-Face Superficial Networks 253
19.4.2 Results for Face-to-Face Self-Disclosing
Networks 254
19.4.3 Results for Text Message-Mediated Superficial
Networks 255
19.4.4 Results for Text Message-Mediated
Self-Disclosing Networks 255
19.5 Discussion 256
20. Differential Impact of Directors Social and Financial Capital
on Corporate Interlock Formation 260
20.1 Bipartite Society: The Individual and the Group 260
20.1.1 Director Capital and Interlock Formation 261
20.2 Data and Measures 262
20.2.1 Social Network Data 262
20.2.2 Actor-Relation Measures 263
20.2.3 Analyses 264
20.3 Model Specification 266
20.3.1 Independent Bivariate Attribute Analysis 266
20.3.2 Purely Structural Effects 266
20.3.3 Models with Attributes: Actor-Relation Effects 266
20.4 Results 267
20.4.1 Results for Independent Bivariate Analysis 267
20.4.2 Results for Purely Structural Effects 267
20.4.3 Results for Models Including Purely Structural
and Actor-Relation Effects 268
20.5 Discussion 270
21. Comparing Networks: Structural Correspondence between
Behavioral and Recall Networks 272
21.1 Relationship between Behavior and Recall 272
21.2 Data and Measures 273
21.2.1 Description of Networks 273
21.2.2 Data Transformations 274
21.2.3 Model Specification 274
21.3 Results 275
21.3.1 Visualization 275
21.4 Preliminary Statistical Analysis 277
21.5 Univariate Models 277
21.6 Models of Recall Networks with Behavioral Networks
as Covariates 278
21.7 Multivariate Models 280
21.8 Discussion 282
SECTION IV. FUTURE
22. Modeling Social Networks: Next Steps 287
22.1 Distinctive Features of ERGMs 287
22.2 Model Specification 289
22.2.1 Dependence Hierarchy 291
22.2.2 Building Model Specifications 296
22.2.3 Models with Latent Variables: Hybrid Forms 297
22.2.4 Assessing Homogeneity Assumptions 299
22.3 General Issues for ERGMs 299
References 303
Index 327
Name Index 331
|
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| dewey-full | 302.3 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 302 - Social interaction |
| dewey-raw | 302.3 |
| dewey-search | 302.3 |
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| discipline | Soziologie Mathematik |
| edition | 1. publ. |
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| spelling | Exponential random graph models for social networks theories, methods, and applications ed. by Dean Lusher ... 1. publ. Cambridge [u.a.] Cambridge University Press 2013 XXII, 336 S. Ill., graph. Darst. 23 cm txt rdacontent n rdamedia nc rdacarrier Structural analysis in the social sciences 35 Hier auch später erschienene, unveränderte Nachdrucke Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd rswk-swf Graphentheoretisches Modell (DE-588)4158055-2 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Netzwerkanalyse Soziologie (DE-588)4205975-6 s Graphentheoretisches Modell (DE-588)4158055-2 s DE-604 Lusher, Dean (DE-588)1023694352 edt Structural analysis in the social sciences 35 (DE-604)BV002814947 35 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025485793&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Exponential random graph models for social networks theories, methods, and applications Structural analysis in the social sciences Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd Graphentheoretisches Modell (DE-588)4158055-2 gnd |
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| title | Exponential random graph models for social networks theories, methods, and applications |
| title_auth | Exponential random graph models for social networks theories, methods, and applications |
| title_exact_search | Exponential random graph models for social networks theories, methods, and applications |
| title_full | Exponential random graph models for social networks theories, methods, and applications ed. by Dean Lusher ... |
| title_fullStr | Exponential random graph models for social networks theories, methods, and applications ed. by Dean Lusher ... |
| title_full_unstemmed | Exponential random graph models for social networks theories, methods, and applications ed. by Dean Lusher ... |
| title_short | Exponential random graph models for social networks |
| title_sort | exponential random graph models for social networks theories methods and applications |
| title_sub | theories, methods, and applications |
| topic | Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd Graphentheoretisches Modell (DE-588)4158055-2 gnd |
| topic_facet | Netzwerkanalyse Soziologie Graphentheoretisches Modell Aufsatzsammlung |
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