Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework
for modeling dense and sparse random graphs with short-or long-tailed degree distributions …

Statistical models for social networks have enabled researchers to study complex social
phenomena that give rise to observed patterns of relationships among social actors and to …

Multilevel network data provide two important benefits for ERG modeling. First, they facilitate
estimation of the decay parameters in geometrically weighted terms for degree and triad …

Statistical inference for exponential-family models of random graphs with dependent edges
is challenging. We stress the importance of additional structure and show that additional …

The last two decades have seen considerable progress in foundational aspects of statistical
network analysis, but the path from theory to application is not straightforward. Two large …

Finite Mixtures of ERGMs for Modeling Ensembles of Networks Page 1 Bayesian Analysis (2022)
17, Number 4, pp. 1153–1191 Finite Mixtures of ERGMs for Modeling Ensembles of Networks …

Graph processes that unfold in continuous time are of obvious theoretical and practical
interest. Particularly useful are those whose long-term behavior converges to a graph …

I congratulate the authors on their timely and insightful article. Since the advent of network
analysis, there has been the question of the meaning of sample size in a network setting …

Exponential family random graph models (ERGMs) can be understood in terms of a set of
structural biases that act on an underlying reference distribution. This distribution determines …

This study advances a theoretical framework of consolidation as a measure of structural
intersectionality and applies it to study interethnic friendships in Western European …