Recovering time-varying networks of dependencies in social and biological studies
- Amr Ahmed and
- Eric P. Xing,1
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 l1-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.
Footnotes
- 1To whom correspondence should be addressed. E-mail: epxing{at}cs.cmu.edu
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Edited by Stephen E. Fienberg, Carnegie Mellon University, Pittsburgh, PA, and approved April 29, 2009
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Author contributions: E.P.X. designed research; A.A. and E.P.X. performed research; A.A. and E.P.X. contributed new reagents/analytic tools; A.A. and E.P.X. analyzed data; and E.P.X. and A.A. wrote the paper.
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The authors declare no conflict of interest.
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This article is a PNAS Direct Submission.
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This article contains supporting information online at www.pnas.org/cgi/content/full/0901910106/DCSupplemental.
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Freely available online through the PNAS open access option.




