Thiemichen, S.S.ThiemichenFriel, NialNialFrielCaimo, AlbertoAlbertoCaimoKauermann, G.G.Kauermann2017-02-202016 Elsev2016-07Social Networkshttp://hdl.handle.net/10197/8366We 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.enThis is the author’s version of a work that was accepted for publication in Social Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Social Networks (VOL 48, ISSUE 2016, (2016)) DOI: 10.1016/j.socnet.2016.01.002.Machine learningStatisticsExponential random graph modelsBayesian inferenceRandom effectsNetwork analysisJournal Article46112810.1016/j.socnet.2016.01.0022016-11-14https://creativecommons.org/licenses/by-nc-nd/3.0/ie/