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Statistical Inference for Complex Networks Workshop, December 3-5, 2008, Santa Fe NM

Organizers: Aaron Clauset (SFI) and Cris Moore (UNM & SFI)

Eric Xing (homepage)

Time (and Space)-Varying Networks: Reverse engineering rewiring social and genetic interactions

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 (or space). While there is a rich literature in 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 talk, I will present a new formalism for modeling network evolution over time based on temporal exponential random graphs, and several new algorithms based on temporal extensions of the sparse graphical logistic regression, for reverse-engineering the latent time/space varying networks. These algorithms can be cast as standard convex-optimization problems and solved efficiently using generic solvers scalable to large networks. I will show some promising results on recovering the latent sequence of temporally rewiring academic social networks from conference proceeding series, and the latent evolving gene networks over more than 4000 genes during the life cycle of Drosophila melanogaster from microarray time course, at a time resolution only limited by sample frequency (i.e., works even when a single snapshot of node-values from each time-specific network is available.)