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 …

A network provides powerful means of representing complex relationships between entities
by abstracting entities as vertices, and relationships as edges connecting vertices in a …

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 …

Our focus is on realistically modeling and forecasting dynamic networks of face-to-face
contacts among individuals. Important aspects of such data that lead to problems with …

Change-point detection in dynamic networks is a challenging task which is particularly due
to the complex nature of temporal graphs. Existing approaches are based on the extraction …

The issue of modelling observations generated in matrix form over time is key in economics,
finance and many domains of application. While it is common to model vectors of …

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 …

Modeling sequential data is essential to many applications such as natural language
processing, recommendation systems, time series predictions, anomaly detection, etc. When …

Real-world graphs like social networks are often evolutionary over time, whose observations
at different timestamps lead to graph sequences. Modeling such evolutionary graphs is …

Latent space models are popular for analyzing dynamic network data. We propose a
variational approach to estimate the model parameters and the latent positions of the nodes …