Detecting Topological Changes in Dynamic Community Networks
Abstract
The study of timevarying (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 model. This model is formed by adding new vertices, and randomly attaching them to the existing nodes. It is a dynamic extension of the wellknown stochastic blockmodel. 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 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 the the theoretical analysis of the test statistic, we perform Monte Carlo simulations of our dynamic graph model to demonstrate that our test can detect changes in graph topology.
 Publication:

arXiv eprints
 Pub Date:
 July 2017
 arXiv:
 arXiv:1707.07362
 Bibcode:
 2017arXiv170707362W
 Keywords:

 Computer Science  Social and Information Networks;
 Computer Science  Discrete Mathematics;
 Physics  Physics and Society