We show that inference based on a variational mean-field approximation to the likelihood of an Exponential-family Random Graph Model (ERGM) 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 situations that the mean-field approximation was designed for.
The statistical modeling of social network data is difficult due to the complex dependence structure of the tie variables. Statistical exponential families of distributions provide a flexible way to model such dependence. They enable the statistical …
The ability to simulate graphs with given properties is important for the analysis of social networks. Sequential importance sampling has been shown to be particularly effective in estimating the number of graphs adhering to fixed marginals and in …
statnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM). The components of the package provide a comprehensive …
Objective: To determine the extent to which men provide a bridge population between commercial sex workers (CSW) and the general female population in Thailand. Design: Sexual network and serological data were collected from a systematic quota sample …