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

  1. Amr Ahmed and
  2. Eric P. Xing,1
  1. Language Technology Institute and Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213

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
  • Edited by Stephen E. Fienberg, Carnegie Mellon University, Pittsburgh, PA, and approved April 29, 2009

  • 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.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/cgi/content/full/0901910106/DCSupplemental.

  • Freely available online through the PNAS open access option.

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