Abstract Exponential Random Graph Models (ERGMs) have gained increasing popularity
over the years. Rooted into statistical physics, the ERGMs framework has been successfully …

Many popular models from the networks literature can be viewed through a common lens of
contingency tables on network dyads, resulting in\emph {log-linear ERGMs}: exponential …

Exponential-family Random Graph Models (ERGMs) are a class of models that is frequently
used for modeling social networks. ERGMs allow structural features as well as covariate …

In general, statistical inference for exponential-family random graph models of dependent
random graphs given a single observation of a random graph is problematic. We show that …

Exchangeable random graphs, which include some of the most widely studied network
models, have emerged as the mainstay of statistical network analysis in recent years …

Large network, as a form of big data, has received increasing amount of attention in data
science, especially for large social network, which is reaching the size of hundreds of …

Continuous mixtures of distributions are widely employed in the statistical literature as
models for phenomena with highly divergent outcomes; in particular, many familiar heavy …

Ensembles of networks arise in various fields where multiple independent networks are
observed, for example, a collection of student networks from different classes. However …

Abstract 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 …