Recovering time-varying networks of dependencies in social and biological studies
Recovering time-varying networks of dependencies in social and biological studies
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DOI：
10.1073/pnas.0901910106
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发表时间：
2009-07-21
影响因子：
11.1
通讯作者：
Xing, Eric P.
中科院分区：
文献类型：
--
作者：
Ahmed, Amr;Xing, Eric P.
在动态系统（例如活细胞或社会社区）中实体之间关系信息的合理表示是一个随机网络，随着时间的流逝，它在拓扑上是重新传播的，在语义上会随着时间的推移而发展。尽管在建模静态或时间不变的网络时有丰富的文献，但是在动态环境中无法观察到网络时，几乎没有做任何事情来恢复网络结构。在本文中，我们提出了一种称为Tesla的机器学习方法，该方法建立在时间平滑的I-1调查逻辑回归形式主义上，该形式主义可以作为标准凸优化问题施放，并通过将通用求解器使用可扩展到大型网络的通用求解器来有效地解决。我们报告了有关恢复模拟时变网络以及从纵向数据重新布线和学术社交网络的潜在序列以及在果蝇时期果蝇Melanogaster生命周期中不断发展的基因网络的潜在序列，以及在造成微段会中不断发展的基因网络的潜在结果。仅通过样本频率限制分辨率的课程。
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 I-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.