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 towards 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  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 datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course.
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.
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towards
toward
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,
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a
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datasets
data sets
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network
networks
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senate
Senate
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gene network which contains more than 4000 genes from
regulatory networks underlying 588 genes across
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Drosophila melanogaster
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from
Drosophila melanogaster from the