Bayesian exponential random graph models with nodal random effects

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Title: Bayesian exponential random graph models with nodal random effects
Authors: Thiemichen, S.
Friel, Nial
Caimo, Alberto
Kauermann, G.
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Date: Jul-2016
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Journal: Social Networks
Volume: 46
Start page: 11
End page: 28
Copyright (published version): 2016 Elsevier
Keywords: Machine learningStatisticsExponential random graph modelsBayesian inferenceRandom effectsNetwork analysis
DOI: 10.1016/j.socnet.2016.01.002
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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