Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

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 …

Information diffusion in online social networks is affected by the underlying network
topology, but it also has the power to change it. Online users are constantly creating new …

Network data often take the form of repeated interactions between senders and receivers
tabulated over time. A primary question to ask of such data is which traits and behaviours …

Time varying problems usually have complex underlying structures represented as dynamic
networks where entities and relationships appear and disappear over time. The problem of …

Important questions in the social sciences are concerned with the circumstances under
which individuals, organizations, or states mutually agree to form social network ties …

Time plays an essential role in the diffusion of information, influence, and disease over
networks. In many cases we can only observe when a node is activated by a contagion …

Ample theoretical work on social networks is explicitly or implicitly concerned with the role of
interpersonal interaction. However, empirical studies to date mostly focus on the analysis of …

We propose a non-parametric link prediction algorithm for a sequence of graph snapshots
over time. The model predicts links based on the features of its endpoints, as well as those of …

We propose three extensions for the relational event framework to model the co-evolution of
multiple network event streams which are increasingly available thanks to the explosive …