Date: **March 27th 2017**

Speaker: **Francois Meyer (University of Colorado, Boulder/INRIA)**

Title:** Detecting Structural Changes in Dynamic Community Networks**

**Abstract**

The study of time-varying (dynamic) networks (graphs) is of fundamental importance for computer network analytics. Several methods have been proposed to detect the effect of significant structural changes in a time series of graphs.

The main contribution of this work is a detailed analysis of a dynamic community graph. This model is formed by adding new vertices, and randomly attaching them to the existing nodes. The goal of the work is to detect the time at which the graph dynamics switches from a normal evolution -- where balanced communities grow at the same rate -- to an abnormal behavior -- where communities start merging.

In order to circumvent the problem of identifying the communities, we use a metric to quantify structural changes as a function of time. The detection of anomalies becomes one of testing the hypothesis that the graph is undergoing a significant structural change.

This is work in collaboration with Peter Wills.