Highly Scalable Maximum Likelihood and Conjugate Bayesian Inference for ERGMs on Graph Sets with Equivalent Vertices

TL;DR

This paper introduces a scalable inference method for ERGMs that efficiently handles large sets of graphs with equivalent vertices, enabling large-scale pooled and Bayesian analysis with minimal additional computational cost.

Contribution

The authors develop a novel approach exploiting exponential family properties to perform scalable pooled and Bayesian ERGM inference, overcoming computational limitations of traditional methods.

Findings

Method achieves linear scaling with number of graphs

Posterior estimates are well-behaved and reliable

Applied successfully to brain networks and protein structures

Abstract

The exponential family random graph modeling (ERGM) framework provides a flexible approach for the statistical analysis of networks. As ERGMs typically involve normalizing factors that are costly to compute, practical inference relies on a variety of approximations or other workarounds. Markov Chain Monte Carlo maximum likelihood (MCMC MLE) provides a powerful tool to approximate the MLE of ERGM parameters, and is feasible for typical models on single networks with as many as a few thousand nodes. MCMC-based algorithms for Bayesian analysis are more expensive, and high-quality answers are challenging to obtain on large graphs. For both strategies, extension to the pooled case - in which we observe multiple networks from a common generative process - adds further computational cost, with both time and memory scaling linearly in the number of graphs. This becomes prohibitive for large networks, or where large numbers of graph observations are available. Here, we exploit some basic properties of the discrete exponential families to develop an approach for ERGM inference in the pooled case that (where applicable) allows an arbitrarily large number of graph observations to be fit at no additional computational cost beyond preprocessing the data itself. Moreover, a variant of our approach can also be used to perform Bayesian inference under conjugate priors, again with no additional computational cost in the estimation phase. As we show, the conjugate prior is easily specified, and is well-suited to applications such as regularization. Simulation studies show that the pooled method leads to estimates with good frequentist properties, and posterior estimates under the conjugate prior are well-behaved. We demonstrate our approach with applications to pooled analysis of brain functional connectivity networks and to replicated x-ray crystal structures of hen egg-white lysozyme.