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Title Exponential random graph models for social networks : theory, methods, and applications / editors, Dean Lusher, Johan Koskinen, Garry Robbins
Published Cambridge : Cambridge University Press, 2013
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Description 1 online resource (xxii, 336 pages)
Series Structural analysis in the social sciences ; 35
Structural analysis in the social sciences ; 35
Contents 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
Summary This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs)
Notes 9.3.2 Network-Dependent Assumptions
Bibliography Includes bibliographical references (pages 303-325) and indexes
Notes Print version record
Subject Social networks -- Mathematical models.
Social networks -- Research -- Graphic methods.
Form Electronic book
Author Koskinen, Johan, editor
Lusher, Dean, editor
Robins, Garry, editor
ISBN 0511894708 (electronic bk.)
0521193567 (hardback)
1139839586 (electronic bk.)
1139841963 (electronic bk.)
9780511894701 (electronic bk.)
9780521193566 (hardback)
9781139839587 (electronic bk.)
9781139841962 (electronic bk.)