Exponential random graph models for social networks : theory, methods, and applications / editors, Dean Lusher, Johan Koskinen, Garry Robbins.

Print version record.

Cover; Exponential Random Graph Models for Social Networks; Structural Analysis in the Social Sciences; Title; Copyright; Dedication; Contents; List of Figures; List of Tables; 1 Introduction; 1.1 Intent of This Book; 1.2 Software and Data; 1.3 Structure of the Book; 1.3.1 Section I: Rationale; 1.3.2 Section II: Methods; 1.3.3 Section III: Applications; 1.3.4 Section IV: Future; 1.4 How To Read This Book; 1.5 Assumed Knowledge of Social Network Analysis; Section I: Rationale; 2 What Are Exponential Random Graph Models?; 2.1 Exponential Random Graph Models: A Short Definition; 2.2 ERGM Theory.

2.3 Brief History of ERGMs2.4 Network Data Amenable to ERGMs; 3 Formation of Social Network Structure; 3.1 Tie Formation: Emergence of Structure; 3.1.1 Formation of Social Ties; 3.1.2 Network Configurations: Consequential Network Patterns and Related Processes; 3.1.3 Local Network Processes; 3.1.4 Dependency (and Theories of Network Dependence); 3.1.5 Complex Combination of Multiple and Nested Social Processes; 3.2 Framework for Explanations of Tie Formation; 3.2.1 Network Self-Organization; 3.2.2 Individual Attributes; 3.2.3 Exogenous Contextual Factors: Dyadic Covariates.

4 Simplified Account of an Exponential Random Graph Model as a Statistical Model4.1 Random Graphs; 4.2 Distributions of Graphs; 4.3 Some Basic Ideas about Statistical Modeling; 4.4 Homogeneity; 5 Example Exponential Random Graph Model Analysis; 5.1 Applied ERGM Example: Communication in "The Corporation"; 5.2 ERGM Model and Interpretation; 5.2.1 Multiple Explanations for Network Structure; Section II: Methods; 6 Exponential Random Graph Model Fundamentals; 6.1 Chapter Outline; 6.2 Network Tie-Variables; 6.3 Notion of Independence; 6.4 ERGMs from Generalized Linear Model Perspective.

6.5 Possible Forms of Dependence6.5.1 Bernoulli Assumption; 6.5.2 Dyad-Independent Assumption; 6.5.3 Markov Dependence Assumption; 6.5.4 Realization-Dependent Models; 6.6 Different Classes of Model Specifications; 6.6.1 Bernoulli Model; 6.6.2 Dyadic Independence Models; 6.6.3 Markov Model; 6.6.4 Social Circuit Models; 6.7 Other Model Specifications; 6.8 Conclusion; 7 Dependence Graphs and Sufficient Statistics; 7.1 Chapter Outline; 7.2 Dependence Graph; 7.2.1 Hammersley-Clifford Theorem and Sufficient Statistics; 7.2.2 Sufficient Subgraphs for Nondirected Graphs.

7.3 Dependence Graphs Involving Attributes7.4 Conclusion; 8 Social Selection, Dyadic Covariates, and Geospatial Effects; 8.1 Individual, Dyadic, and Other Attributes; 8.2 ERGM Social Selection Models; 8.2.1 Models for Undirected Networks; 8.2.2 Models for Directed Networks; 8.2.3 Conditional Odds Ratios; 8.3 Dyadic Covariates; 8.4 Geospatial Effects; 8.5 Conclusion; 9 Autologistic Actor Attribute Models; 9.1 Social Influence Models; 9.2 Extending ERGMs to Distribution of Actor Attributes; 9.3 Possible Forms of Dependence; 9.3.1 Independent Attribute Assumption.

9.3.2 Network-Dependent Assumptions.

Includes bibliographical references (pages 303-325) and indexes.

This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs).

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