Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 106, Issue 29, Pages 11878-11883Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.0901910106
Keywords
evolving network; social network; gene network; lasso; Markov random field
Categories
Funding
- National Science Foundation (NSF) [DBI-0546594, IIS-0713379]
- Defense Advanced Research Projects Agency Award [Z931302]
- Office of Naval Research Award [N000140910758]
- Alfred P. Sloan Research Fellowship
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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 I-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 and on reverse engineering the latent sequence of temporally rewiring political and academic social networks from longitudinal data, and the evolving gene networks over >4,000 genes during the life cycle of Drosophila melanogaster from a microarray time course at a resolution limited only by sample frequency.
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