#### Document Type

Article

#### Rights

This item is available under a Creative Commons License for non-commercial use only

#### Disciplines

Statistics

#### Abstract

Exponential random graph models are a class of widely used exponential family models for social networks. The topological structure of an observed network is modelled by the relative prevalence of a set of local sub-graph configurations termed network statistics. One of the key tasks in the application of these models is which network statistics to include in the model. This can be thought of as statistical model selection problem. This is a very challenging problem---the posterior distribution for each model is often termed ``doubly intractable'' since computation of the likelihood is rarely available, but also, the evidence of the posterior is, as usual, intractable. The contribution of this paper is the development of a fully Bayesian model selection method based on a reversible jump Markov chain Monte Carlo algorithm extension of Caimo & Friel (2011) which estimates the posterior probability for each competing model.

#### DOI

http://doi.org10.1016/j.socnet.2012.10.003

#### Recommended Citation

Caimo, A. and Friel, N. (2013), Bayesian Model Selection for Exponential Random Graph Models. *Social Networks*, 35(1), 11 – 24. 2013.
http://dx.
doi.org/10.1016/j.socnet.2012.10.003

## Publication Details

Social Networks, 35(1), 11 – 24. 2013http://www.journals.elsevier.com/social-networks/