Learning Time-Varying Graphs using Temporally Smoothed L1-Regularized Logistic Regression

Abstract Aplausible representation of the relational information among,entities in dynamic,systems such as a living cell or a social community,is a stochastic network which is topologicallyrewiring and semantically evolving over time. While there is a rich literature on modeling static or temporally invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks ,when ,they are not observable. In this paper we present ,an optimization-based approach for recovering time-evolving discrete networks from time stamped,node samples from the network. We cast this graphical model learning problem as a temporally smoothed,L1-regularized logistic regression problem which can be formulated ,and solved efficiently using standard convex-optimization solvers scalable to large networks. We report ,promising ,results on recovering ,the dynamics ,of the coauthorship-keyword academic