Hierarchical DP ERGM Mixtures of Heterogeneous Network Data

Oregon Convention Center 

A concern when modeling ensembles of networks obtained from diverse sources is the presence of heterogeneity in the determinants of network structure; a property captured well by Dirichlet Process mixtures. Recently, work using DP mixtures of exponential family random graph models has shown that the approximate likelihood can be successfully employed for posterior inference on hidden populations, enabling broader families of such mixtures. Here, we consider hierarchical DP ERGM mixtures with both partial pooling (effects treated homogeneously across networks) and heterogeneity in included effects (allowing different effects between different mixture components). The former allows for semi-parametric estimation of given effects of interest (controlling for heterogeneity in other effects), while the latter allows for model averaging (can be used for model selection & clustering applications). The prior on the concentration parameter allows us to assign minimal prior weight to undesirable size distributions while adaptively assessing hidden population sizes. We evaluate the behavior of extended DP ERGM mixtures via simulation and display an application to heterogeneous network data.

Networks

Exponential Family Random Graph Models

Dirichlet Process

Bayesian statistics

Hierarchical Modelling

Data Clustering 

Section on Bayesian Statistical Science