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 efforts have gone into the development of statistical models for analyzing data in
the form of networks, such as social networks. Most existing work has focused on modeling …

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 …

Optimal change point detection and localization in sparse dynamic networks Page 1 The Annals
of Statistics 2021, Vol. 49, No. 1, 203–232 https://doi.org/10.1214/20-AOS1953 © Institute of …

There has been great interest in recent years on statistical models for dynamic networks. In
this paper, I propose a stochastic block transition model (SBTM) for dynamic networks that is …

The development of statistical models for continuous-time longitudinal network data is of
increasing interest in machine learning and social science. Leveraging ideas from survival …

Significant efforts have gone into the development of statistical models for analyzing data in
the form of networks, such as social networks. Most existing work has focused on modeling …

Network modeling and analysis has become a fundamental tool for studying various
complex systems. This paper proposes an extension of statistical monitoring to network …

Although much of the focus of statistical works on networks has been on static networks,
multiple networks are currently becoming more common among network data sets. Usually …

Relational data—like graphs, networks, and matrices—is often dynamic, where the relational
structure evolves over time. A fundamental problem in the analysis of time-varying network …