Open Access
March 2010 Estimating time-varying networks
Mladen Kolar, Le Song, Amr Ahmed, Eric P. Xing
Ann. Appl. Stat. 4(1): 94-123 (March 2010). DOI: 10.1214/09-AOAS308


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 l1-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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Mladen Kolar. Le Song. Amr Ahmed. Eric P. Xing. "Estimating time-varying networks." Ann. Appl. Stat. 4 (1) 94 - 123, March 2010.


Published: March 2010
First available in Project Euclid: 11 May 2010

zbMATH: 1189.62142
MathSciNet: MR2758086
Digital Object Identifier: 10.1214/09-AOAS308

Keywords: graphical models , High-dimensional statistics , kernel smoothing , Markov random fields , semi-parametric estimation , structure learning , Time-varying networks , total-variation regularization

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.4 • No. 1 • March 2010
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