Bayesian Exponential Random Graph Models with Nodal Random Eﬀects
Refereed Original Article
We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random eﬀects to compensate for heterogeneity in the nodes of a network. The Bayesian framework for ERGMs proposed by Caimo and Friel (2011) yields the basis of our modelling algorithm. A central question in network models is the question of model selection and following the Bayesian paradigmwefocusonestimatingBayesfactors. Todosowedevelopanapproximate but feasible calculation of the Bayes factor which allows one to pursue model selection. Two data examples and a small simulation study illustrate our mixed model approach and the corresponding model selection.
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National University of Ireland, Dublin (UCD)
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