| Event Description: Peter Wills, Department of Applied Mathematics, University of Colorado Boulder Detecting Topological Changes in Dynamic Community Networks The study of time-varying networks is of fundamental importance for computer network analytics. Dynamic networks provide models for social networks, and are used to decode functional connectivity in neuroscience and in biology. Several methods have been proposed to detect the effect of significant structural changes (e.g., changes in topology, connectivity, or relative size of the communities in a community graph) in a time series of graphs. The main contribution of this work is a detailed analysis of the dynamic stochastic blockmodel, a model for a random growing graph with community structure. The goal of the work is to detect the time at which the graph dynamics switches from a normal evolution -- where two balanced communities grow at the same rate -- to an abnormal behavior -- where the two communities are merging. Because the evolution of the graph is stochastic, one expects random fluctuation of the graph geometry. The challenge is to detect an anomalous event under normal random variation. In order to circumvent the problem of decomposing each graph into communities, we use a metric to quantify changes in the graph topology as a function of time. The detection of anomalies becomes one of testing the hypothesis that the graph is undergoing a significant structural change. In addition to the theoretical analysis of the statistical test, we conduct several experiments on synthetic and real dynamic networks, and we demonstrate that our test can detect changes in graph topology. This work is in collaboration with François Meyer. |
| Location Information: Main Campus - Engineering Office Tower (View Map) 1111 Engineering DR Boulder, CO Room: 226: Applied Math Conference Room |
| Contact Information: Name: Ian Cunningham Phone: 303-492-4668 Email: amassist@colorado.edu |