6 research outputs found
Bayesian Exponential Random Graph Models with Nodal Random Effects
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.Comment: 23 pages, 9 figures, 3 table
Missing Data Augmentation for Bayesian Exponential Random Multi-Graph Models
In this paper we present an estimation algorithm for Bayesian multiplex exponential random graphs (BmERGMs) under missing network data. Social actors are often connected with more than one type of relation, thus forming a multiplex network. It is important to consider these multiplex structures simultaneously when analyzing a multiplex network. The importance of proper models of multiplex network structures is even more pronounced under the issue of missing network data. The proposed algorithm is able to estimate BmERGMs under missing data and can be used to obtain proper multiple imputations for multiplex network structures. It is an extension of Bayesian exponential random graphs (BERGMs) as implemented in the Bergm package in R. We demonstrate the algorithm on a well known example, with and without artificially simulated missing data