Graph processes that unfold in continuous time are of obvious theoretical and practical interest. Particularly useful those whose long-term behavior converges to a graph distribution known form. Here, we review some the conditions for such convergence, provide examples novel and/or do so. These include subfamilies well-known stochastic actor-oriented models, as well continuum extensions tempora...

Contrastive divergence (CD) is a promising method of inference in high dimensional distributions with intractable normalizing constants, however, the theoretical foundations justifying its use are somewhat weak. This document proposes a framework for understanding CD inference, including how and when it works. It provides multiple justifications for the CD moment conditions, including framing t...

Health 2.0 provides patients an unprecedented way to connect with each other online. However, less attention has been paid to how patient collaborative friendships form in online healthcare communities. This study examines the relationship between collaborative friendship formation and patients’ characteristics. Results from Exponential Random Graph Model (ERGM) analysis indicate that gender ho...

We review the broad range of recent statistical work in social network models, with emphasis on computational aspects of these methods. Particular focus is applied to exponential-family random graph models (ERGM) and latent variable models for data on complete networks observed at a single time point, though we also briefly review many methods for incompletely observed networks and networks obs...

This paper explores how bilateral and multilateral clustering are embedded in a multilevel system of interdependent networks. We argue that, in a complex system such as global fisheries governance, in which bilateral and multilateral relations are themselves interrelated, embeddedness cannot be reduced to unipartite or bipartite clustering but implicates potential multilevel closure. We elabora...

The conventional exponential family random graph model (ERGM) parameterization leads to a baseline density that is constant in graph order (i.e., number of nodes); this is potentially problematic when modeling multiple networks of varying order. Prior work has suggested a simple alternative that results in constant expected mean degree. Here, we extend this approach by suggesting another altern...

This paper explains the epidemic spread using social network analysis, based on data from first three months of 2020 COVID-19 outbreak across world and in Canada. A is defined visualization used to understand coronavirus among countries impact other The degree centrality identify main influencing countries. Exponential Random Graph Models (ERGM) are processes that influence link creation betwee...