We consider partially observed network data as defined in Handcock and Gile (2010). More
specifically we introduce an elaboration of the Bayesian data augmentation scheme of …

The main challenges in estimating strategic network formation models are the presence of
multiple equilibria, and the fact that the number of possible network configurations increases …

We describe a network clustering framework, based on finite mixture models, that can be
applied to discrete-valued networks with hundreds of thousands of nodes and billions of …

Exponential random graph models are a class of widely used exponential family models for
social networks. The topological structure of an observed network is modelled by the relative …

We study the evolution of the network of foreign direct investment (FDI) in the international
electricity industry during the period 1994–2003. We assume that the ties in the network of …

Gibbs random fields play an important role in statistics. However, they are complicated to
work with due to an intractability of the likelihood function and there has been much work …

Exponential random graph models have been widely used in social network analysis.
However, these models are extremely difficult to handle from a statistical viewpoint, because …

The exponential random graph model (ERGM) plays a major role in social network analysis.
However, parameter estimation for the ERGM is a hard problem due to the intractability of its …

Evidence of social interactions is found everywhere for various kinds of economic outcomes;
for example, students' academic performance, use of tobacco and alcohol, and the spread of …