The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

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 …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

We present a selective review of statistical modeling of dynamic networks. We focus on
models with latent variables, specifically, the latent space models and the latent class …

Protein–protein interactions are crucial in many biological pathways and facilitate cellular
function. Investigating these interactions as a graph of pairwise interactions can help to gain …

We study synthetic temporal networks whose evolution is determined by stochastically
evolving node variables—synthetic analogues of, eg, temporal proximity networks of mobile …

We propose a dynamic network model where two mechanisms control the probability of a
link between two nodes:(i) the existence or absence of this link in the past, and (ii) node …

Network latent space models assume that each node is associated with an unobserved
latent position in a Euclidean, and such latent variables determine the probability of two …

Traditional measurement models assume that all item responses correlate with each other
only through their underlying latent variables. This conditional independence assumption …

We consider the problem of embedding a dynamic network, to obtain time-evolving vector
representations of each node, which can then be used to describe changes in behaviour of …