ESTIMATING TIME-VARYING NETWORKS
ESTIMATING TIME-VARYING NETWORKS
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DOI：
10.1214/09-aoas308
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发表时间：
2010-03-01
影响因子：
1.8
通讯作者：
Xing, Eric P.
中科院分区：
文献类型：
--
作者：
Kolar, Mladen;Song, Le;Xing, Eric P.
随机网络是动态系统中实体之间关系信息的一种合理表示，如活细胞或社会社区。虽然有丰富的文献从观测数据估计静态或时间不变的网络，但很少有人从实体属性的时间序列估计时变网络。本文提出了两种估计时变网络的新的机器学习方法，这两种方法都建立在时间光滑的L(1)-正则化Logistic回归形式基础上，可以转化为标准的凸优化问题，并使用可扩展到大型网络的通用求解器来有效地求解。我们报告了在恢复模拟时变网络方面的有希望的结果。对于真实的数据集，我们反向工程了来自美国参议院投票记录的参议员之间的潜在政治网络重新布线的潜在序列，以及从微阵列时间进程中隐藏在果蝇生命周期中588个基因的潜在不断演变的调控网络。
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 l(1)-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.