Bayesian inference for exponential random graph models

Exponential random graph models are extremely difficult models to handle from a statistical viewpoint, since their normalising constant, which depends on model parameters, is available only in very trivial cases. We show how inference can be carried out in a Bayesian framework using a MCMC algorithm...

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Bibliographic Details
Published inSocial networks Vol. 33; no. 1; pp. 41 - 55
Main Authors Caimo, Alberto, Friel, Nial
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 2011
Elsevier
Subjects
Algorithms
Bayesian method
Exponential random graph models
History, theory and methodology
Markov chain Monte Carlo
Methodology
Research methods
Respect
Social network analysis
Social networks
Sociology
Statistics
Exponential random graph models
Markov chain Monte Carlo
Social network analysis
Model
Graphs
Markov's chain
Statistics
Standardization
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Summary:Exponential random graph models are extremely difficult models to handle from a statistical viewpoint, since their normalising constant, which depends on model parameters, is available only in very trivial cases. We show how inference can be carried out in a Bayesian framework using a MCMC algorithm, which circumvents the need to calculate the normalising constants. We use a population MCMC approach which accelerates convergence and improves mixing of the Markov chain. This approach improves performance with respect to the Monte Carlo maximum likelihood method of Geyer and Thompson (1992).
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ISSN:0378-8733
1879-2111
DOI:10.1016/j.socnet.2010.09.004