UCLA

Networks are often a useful way to represent complex social and economic systems. Stochastic models for such networks should parsimoniously reflect the complexity while being both interpretable and tractable. The class of Exponential-family Random Graph Models (ERGMs) is very expressive and flexible in its representation of complex phenomena. However, the estimation and fitting of realistic ERGMs are hampered by the computational challenges of likelihood-based inference. Recently, a deterministic method based on a variational mean-field approximation was proposed that is much less expensive to compute than the standard simulation-based method. In this work, we show that the proposed method performs poorly for realistic models. We show that established alternatives, including MCMC-MLE, MPLE, sampled MPLE, and tapered ERGM-based inference, achieve superior accuracy and computational performance in the situations that the mean-field approximation was designed for.