Dynamic network summarization using convex optimization

The analysis of networks has been much of interest in many fields of research ranging from neuroscience to sociology. Until recently, the major focus of network analysis has been on static networks but there is a growing need to analyze dynamic networks or graphs which evolve over time and have changing topology. One fundamental goal in analyzing dynamic networks is to infer the long term connectivity patterns that can summarize and represent the network with minimum redundancy. In this paper, we propose a signal processing framework which can both determine the transient and stationary parts of the dynamic graphs and summarize network activity with a few number of representative networks. The performance of the proposed method is illustrated for both simulated dynamic network models and real social networks.