Recovering time-varying networks of dependencies in social and biological studies.
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 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.
Ahmed
Amr
A
Language Technology Institute and Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Xing
Eric P
EP
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D004331:Drosophila melanogaster / Q000235:genetics / Q000254:growth & development; D035082:Federal Government; D053263:Gene Regulatory Networks; D008954:Models, Biological; D011643:Publishing; D012044:Regression Analysis; D012944:Social Support; D013997:Time Factors; D014481:United States