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

@article{citeulike:5220842, abstract = {10.1073/pnas.0901910106 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 -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 from a microarray time course at a resolution limited only by sample frequency.}, author = {Ahmed, Amr and Xing, Eric P.}, citeulike-article-id = {5220842}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0901910106}, citeulike-linkout-1 = {http://www.pnas.org/content/106/29/11878.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/106/29/11878.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19570995}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19570995}, day = {21}, doi = {10.1073/pnas.0901910106}, journal = {Proceedings of the National Academy of Sciences}, keywords = {2009, graphic, learning, network, pnas, sparse}, month = {July}, number = {29}, pages = {11878--11883}, posted-at = {2009-08-26 06:46:08}, priority = {5}, title = {Recovering time-varying networks of dependencies in social and biological studies}, url = {http://dx.doi.org/10.1073/pnas.0901910106}, volume = {106}, year = {2009} }

TY - JOUR ID - citeulike:5220842 L3 - citeulike-article-id:5220842 N2 - 10.1073/pnas.0901910106 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 -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 from a microarray time course at a resolution limited only by sample frequency. IS - 29 JF - Proceedings of the National Academy of Sciences EP - 11883 TI - Recovering time-varying networks of dependencies in social and biological studies VL - 106 SP - 11878 KW - 2009 KW - graphic KW - learning KW - network KW - pnas KW - sparse AU - Ahmed, Amr AU - Xing, Eric PY - 2009/07/21/ UR - http://dx.doi.org/10.1073/pnas.0901910106 ER -