This article discusses the stochastic actor-oriented model for analyzing panel data of
networks. The model is defined as a continuous-time Markov chain, observed at two or more …

Statistical models for social networks as dependent variables must represent the typical
network dependencies between tie variables such as reciprocity, homophily, transitivity, etc …

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

Modern multilevel analysis, whereby outcomes of individuals within groups take into account
group membership, has been accompanied by impressive theoretical development (eg …

We introduce a method for the theoretical analysis of exponential random graph models.
The method is based on a large-deviations approximation to the normalizing constant …

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) provide a principled and flexible way to
model and simulate features common in social networks, such as propensities for …

Exponential random graph models are extremely difficult models to handle from a statistical
viewpoint, since their normalising constant, which depends on model parameters, is …

The growing availability of network data and of scientific interest in distributed systems has
led to the rapid development of statistical models of network structure. Typically, however …

Community detection is the task of detecting hidden communities from observed
interactions. Guaranteed community detection has so far been mostly limited to models with …