The development of stochastic models for the analysis of social networks is an important
growth area in contemporary statistics. The last few decades have witnessed the rapid …

The advent and availability of technology has brought us closer than ever through social
networks. Consequently, there is a growing emphasis on mining social networks to extract …

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 …

Many real-world networks are theorized to have core-periphery structure consisting of a
densely-connected core and a loosely-connected periphery. While this phenomenon has …

Dynamic Network Actor Models (DyNAMs) assume that an observed sequence of relational
events is the outcome of an actor-oriented decision process consisting of two decision …

The brain can be modelled as a network with nodes and edges derived from a range of
imaging modalities: the nodes correspond to spatially distinct regions and the edges to the …

Using original data that we have collected on referral relations between 110 hospitals
serving a large regional community, we show how recently derived Bayesian exponential …

Models with intractable likelihood functions arise in areas including network analysis and
spatial statistics, especially those involving Gibbs random fields. Posterior parameter …