Networks are ubiquitous in science. They have also become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …

Host social structure is fundamental to how infections spread and persist, and so the
statistical modelling of static and dynamic social networks provides an invaluable tool to …

Having the ability to work with complex models can be highly beneficial. However, complex
models often have intractable likelihoods, so methods that involve evaluation of the …

In this article I provide a (selective) review of the recent econometric literature on networks. I
start with a discussion of developments in the econometrics of group interactions. I …

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 …

Stochastic blockmodels and variants thereof are among the most widely used approaches to
community detection for social networks and relational data. A stochastic blockmodel …

Models with intractable normalizing functions arise frequently in statistics. Common
examples of such models include exponential random graph models for social networks and …

Objective Food insecurity is a significant and growing concern undermining the wellbeing of
30% of the global population. Food in/security is a complex construct consisting of four …

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 …

Exponential random graph models (ERGMs) are widely used for modeling social networks
observed at one point in time. However the computational difficulty of ERGM parameter …