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 …

Significant progress has been made recently on theoretical analysis of estimators for the
stochastic block model (SBM). In this paper, we consider the multi-graph SBM, which serves …

Networks have become indispensable and ubiquitous structures in many fields to model the
interactions among different entities, such as friendship in social networks or protein …

We present a probabilistic model for learning from dynamic relational data, wherein the
observed interactions among networked nodes are modeled via the Bernoulli Poisson link …

Modeling time series of multilayer network data is challenging due to the peculiar
characteristics of real-world networks, such as sparsity and abrupt structural changes …

The mutually-exciting Hawkes process (ME-HP) is a natural choice to model reciprocity,
which is an important attribute of continuous-time edge (dyadic) data. However, existing …

Background Artificial intelligence can be trained to outperform dermatologists in image‐
based skin cancer diagnostics. However, the networks' sensitivity to biases and overfitting …

In this paper we propose a Bayesian nonparametric approach to modelling sparse time-
varying networks. A positive parameter is associated to each node of a network, which …

Understanding the structure and dynamics of scientific research, ie, the science of science
(SciSci), has become an important area of research in order to address imminent questions …

The spread in the use of tracking systems in sport has made fine-grained spatiotemporal
analysis a primary focus of an emerging sports analytics industry. Recently publicized …