Subject(s) 
Social networks  Mathematical models


Social networks  Research  Graphic methods

Physical Description 
xxii, 336 pages ; illustrations ; 24 cm 
Content Type 
text 
Media Type 
unmediated 
Carrier Type 
volume 
Note 
Includes bibliographical references (pages 303325) and indexes 
Contents 
1. Introduction / Dean Lusher, Johan Koskinen and Garry Robins  2, What are exponential random graph models / Garry Robins and Dean Lusher  3, The formation of social network structure / Dean Lusher and Garry Robins  4. Simplified account of exponential random graph model as a statistical model / Garry Robins and Dean Lusher  5, Example of exponential random graph model analysis / Dean Lusher and Garry Robins  6. Exponential random graph model fundamentals / Johan Koskinene and Galina Daragonova  7. Dependence graphs and sufficient statistics / Johan Koskinen and Galina Daragonova  8. Social selection, dyadic covariates, and geospatial effects / Garry Robins and Galina Daragonova  9. Autologistic actor attribute models / Galina Daragonova and Garry Robins  10. Exponential random graph model extensions: models for multiple networks and bipartite networks / Peng Wang  11. 10. Longitudinal models / Tom Snijders and Johan Koskinen  g12. Simulation, estimation, and goodness of fit / Johan Koskinen and Tom Snijders  13. Illustrations: simulation, estimation and goodness of fit / Garry Robins and Dean Lusher  14. Personal attitudes, perceived attitudes, and social structures: a social selection model / Dean Lusher and Garry Robins  15. How to close a hole: exploring alternative closure mechanisms in interorganizational networks / Alessandro Lomi and Francesca Pallotti  16. Interdependencies between working relations: multivariate ERGMs for advice and satisfaction / Yu Zhao and Olaf Rank  16. Brain, brawn, or optimism?: structure and correlates of emergent military leadership / Yuval Kalish and Gil Luria  18. Autologistic actor attribute model analysis of unemployment: dual importance of who you know and where you live / Galina Daragonova and Pip Pattison  19. Longitudinal changes in facetoface and text messagemediated friendship networks / Tasuku Igarashi  20. Differential impact of directors' social and financial capital on corporate interlock formation / Nicholas Harrigan and Matthew Bond  21. Comparing networks: a structural correspondence between behavioural and recall networks / Eric Quintane  22. Modeling social networks: next steps / Pip Pattison and Tom Snijders 
Summary 
"This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs), as well as a compendium of ERGM methods and illustrative applications" Provided by publisher 

"Exponential random graph models (ERGMs) are a class of statistical models for social networks. They account for the presence (and absence) of network ties and so provide a model for network structure. An ERGM models a given network in terms of small local tiebased structures, such as reciprocated ties and triangles. A social network can be thought of as being built up of these local patterns of ties, called network configurations xe "network configurations" , which correspond to the parameters in the model. Moreover, these configurations can be considered to arise from local social processes, whereby actors in the network form connections in response to other ties in their social environment. ERGMs are a principled statistical approach to modeling social networks. They are theorydriven in that their use requires the researcher to consider the complex, intersecting and indeed potentially competing theoretical reasons why the social ties in the observed network have arisen. For instance, does a given network structure occur due to processes of homophily xe "actorrelation effects:homophily" , xe "homophily" \t "see actorrelation effects" reciprocity xe "reciprocity" , transitivity xe "transitivity" , or indeed a combination of these? By including such parameters together in the one model a researcher can test these effects one against the other, and so infer the social processes that have built the network. Being a statistical model, an ERGM permits inferences about whether, in our network of interest, there are significantly more (or fewer) reciprocated ties, or triangles (for instance), than we would expect" Provided by publisher 
Series 
Structural analysis in the social sciences ; 35

Alternate Author 
Lusher, Dean


Koskinen, Johan


Robins, Garry

