Given a sequence of random (directed and weighted) graphs, we address the problem of
online monitoring and detection of changes in the underlying data distribution. Our idea is to …

This paper studies change point detection in time series of networks, with the Separable
Temporal Exponential-family Random Graph Model (STERGM). Dynamic network patterns …

This paper proposes a simple generative model to detect change points in time series of
graphs. The proposed framework consists of learnable prior distributions for low …

This paper proposes a generative model to detect change points in time series of graphs.
The proposed framework consists of learnable prior distributions for low-dimensional graph …

Due to political, cultural, and social reasons, monitoring social network has increasingly
attracted the attention of both practitioners and scholars. Most studies in this area suffer from …

Networks or graphs are often used to represent relational phenomena in numerous
domains, and relational phenomena by nature progress in time. Devising powerful models …

Existing methods for graph change point detection focus on binary graphs, limiting their
ability to capture the richer information available in weighted graphs. This work introduces a …

With the increasing popularity of graph-based methods for dimensionality reduction and
representation learning, node embedding functions have become important objects of study …