Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications [Paperback]

This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs).The integrated chapters of this edited book provide a self-contained, exhaustive account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs), as well as a compendium of ERGM methods and illustrative applications. The reader is given sufficient detail to specify ERGMs, fit them to data with any of the available software packages, and interpret the results.The integrated chapters of this edited book provide a self-contained, exhaustive account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs), as well as a compendium of ERGM methods and illustrative applications. The reader is given sufficient detail to specify ERGMs, fit them to data with any of the available software packages, and interpret the results.Exponential random graph models (ERGMs) are increasingly applied to observed network data and are central to understanding social structure and network processes. The chapters in this edited volume provide the theoretical and methodological underpinnings of ERGMs, including models for univariate, multivariate, bipartite, longitudinal, and social-influence type ERGMs. Each method is applied in individual case studies illustrating how social science theories may be examined empirically using ERGMs. The authors supply the reader with sufficient detail to specify ERGMs, fit them to data with any of the available software packages, and interpret the results.Introduction Dean Lusher, Johan Koskinen and Garry Robins; 1. What are exponential random graph models Garry Robins and Dean Lusher; 2. The formation of social network structure Dean Lusher and Garry Robins; 3. A simplified account of ERGM as a statistical model Garry Robins and Dean Lusher; 4. An example of ERGM analysis Dean Lusher and Garry Robins; 5. Exponential random graph model fundamentals Johan Koskinel“0