Recovering time-varying networks of dependencies in social and biological studies.

ABSTRACT: 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 that is topologically rewiring and semantically evolving over time. Although there is a rich literature in modeling static or temporally invariant networks, little has been done toward recovering the network structure when the networks are not observable in a dynamic context. In this article, we present a machine learning method called TESLA, which builds on a temporally smoothed l(1)-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently by using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks an

SUBMITTER: Ahmed A 

PROVIDER: S-EPMC2704856 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature