Melbourne School of Psychological Sciences - Research Publications

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Using latent variables to account for heterogeneity in exponential family random graph models

Koskinen, J ; Ermakov, SM ; Melas, V ; Pepelyshev, AN (Saint Petersburg State University, 2009)

We consider relaxing the homogeneity assumption in exponential family random graph models (ERGMs) using binary latent class indicators. This may be interpreted as combining a posteriori blockmodelling with ERGMs, relaxing the independence assumptions of the former and the homogeneity assumptions of the latter. We propose a Markov chain Monte Carlo al- gorithm for drawing from the joint posterior of the model parameters and latent class indicators