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 …

This study shows that residual variation can cause problems related to scaling in
exponential random graph models (ERGM). Residual variation is likely to exist when there …

Statistical models for social networks, such as exponential random graphs (ERGMs), have
increasingly been used by organizational scholars to study the social interactions inside …

Attention to informal communication networks within public organizations has grown in
recent decades. While research has documented the role of individual cognition and social …

A new modelling approach for the analysis of weighted networks with ordinal/polytomous
dyadic values is introduced. Specifically, it is proposed to model the weighted network …

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 exponential family random graph modeling (ERGM) framework provides a highly
flexible approach for the statistical analysis of networks (ie, graphs). As ERGMs with dyadic …

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 …

A paucity of research investigates the heterogeneity in global R&D collaboration across
climate change mitigation technological fields, limiting understanding of major players, inter …

Multi-layer networks arise when more than one type of relation is observed on a common set
of actors. Modeling such networks within the exponential-family random graph (ERG) …