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Abstract
Stochastic networks are a plausible representation of the relational information among
entities in dynamic systems such as living cells or social communities. While there
is a rich literature in estimating a static or temporally invariant network from observation
data, little has been done toward estimating time-varying networks from time series
of entity attributes. In this paper we present two new machine learning methods for
estimating time-varying networks, which both build on a temporally smoothed \(l_1\)-regularized
logistic regression formalism that can be cast as a standard convex-optimization problem
and solved efficiently using generic solvers scalable to large networks. We report
promising results on recovering simulated time-varying networks. For real data sets,
we reverse engineer the latent sequence of temporally rewiring political networks
between Senators from the US Senate voting records and the latent evolving regulatory
networks underlying 588 genes across the life cycle of Drosophila melanogaster from
the microarray time course.
Comments Published in at http://dx.doi.org/10.1214/09-AOAS308 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org)