A plausible 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 topologically rewiring 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 social network in the NIPS conference.

16 pages

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