Bayesian exponential random graph models with nodal random effects

Abstract 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 paradigm we focus on estimating Bayes factors. To do so we develop an approximate but feasible calculation of the Bayes factor which allows one to pursue model selection. Three data examples and a small simulation study illustrate our mixed model approach and the corresponding model selection.

• Publication year

  2014

• Venue

  Soc. Networks

• Publication date

  2014-07-25

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1016/j.socnet.2016.01.002arXiv 1407.6895
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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