Recent developments in exponential random graph (p*) models for social networks

@article{Robins2007RecentDI,
  title={Recent developments in exponential random graph (p*) models for social networks},
  author={Garry Robins and Tom A. B. Snijders and Peng Wang and Mark S. Handcock and Philippa Pattison},
  journal={Soc. Networks},
  year={2007},
  volume={29},
  pages={192-215}
}
This article reviews new specifications for exponential random graph models proposed by Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handcock, M., 2006. New specifications for exponential random graph models. Sociological Methodology] and demonstrates their improvement over homogeneous Markov random graph models in fitting empirical network data. Not only do the new specifications show improvements in goodness of fit for various data sets, but they also help to avoid the… Expand
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The homogeneous Markov random graph models of Frank and Strauss are not appropriate for many observed networks, whereas the new model specifications of Snijders et al. are more suitable for social networks. Expand
New Specifications for Exponential Random Graph Models
The most promising class of statistical models for expressing structural properties of social networks observed at one moment in time is the class of exponential random graph models (ERGMs), alsoExpand
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This article provides an introductory summary of the formulation and application of exponential random graph models for social networks. In this approach, the possible ties among nodes of a networkExpand
Advances in exponential random graph (p*) models applied to a large social network
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This paper applies advances in both model parameterizations and computational algorithms to an examination of the structure observed in an adolescent friendship network of 1,681 actors from the National Longitudinal Study of Adolescent Health (AddHealth). Expand
Advances in exponential random graph (p*) models
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This special edition of Social Networks is argued that, due to very recent progress in the framework of exponential random graph models, the authors are now much closer to the goal of obtaining good statistical models for social networks than they have ever been before. Expand
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