Bayesian Exponential Random Graph Models with Nodal Random Effects

Authors

Stephanie Thiemichen, Ludwig Maximilians Universitat, MunchenFollow
Nial Friel, University College DublinFollow
Alberto Caimo, Technological University DublinFollow
Goeran Kauermann, Ludwig Maximilians Universitat, MunchenFollow

Document Type

Article

Rights

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

Disciplines

Statistics

Publication Details

Social Networks. 46, 11 – 28, 2016.

http://www.sciencedirect.com/science/journal/03788733

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. Two data examples and a small simulation study illustrate our mixed model approach and the corresponding model selection.

DOI

http://doi.org10.1016/j.socnet.2016.01.002

Recommended Citation

Thiemichen, S., Friel, N., Caimo, A. and Kauermann, G. (2016), "Bayesian Exponential Random Graph Models with Nodal Random Effects," Social Networks. 46, 11 – 28, July 2016. doi:10.1016/j.socnet.2016.01.002