Statistical Consistency for Change Point Detection and Community Estimation in Time-Evolving Dynamic Networks
Statistical Consistency for Change Point Detection and Community Estimation in Time-Evolving Dynamic Networks
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DOI：
10.1109/tsipn.2022.3156434
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发表时间：
2022
影响因子：
3.2
通讯作者：
Cong Xu;Thomas C.M. Lee
中科院分区：
文献类型：
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作者：
Cong Xu;Thomas C.M. Lee
假设观察到网络的时间序列。众所周知，网络的概率行为不会随着时间的推移而改变，除了在少数时间点。这些时间点通常被称为变化点，其数量和位置是未知的。本文提出了一种自动估计这种变化点和社区结构的网络的方法。所提出的方法调用最小描述长度的原则，推导出一个模型选择标准，其中最好的估计被定义为它的最小值。结果表明，这种选择标准产生一致的估计的变化点以及社区结构。对于实际的最小化的选择标准，自底向上的搜索算法，结合EM算法与变分逼近的开发。通过一系列的数值实验和应用程序的一些真实的数据集所示的所提出的方法的有前途的经验性质。据作者所知，该方法是最早在时间演化网络的变点检测背景下提供一致估计的方法之一。
Suppose a time sequence of networks is observed. It is known that the probabilistic behaviors of the networks do not change over time, except at a few time points. These time points are usually called change points, whose number and locations are unknown. This paper proposes a method for automatically estimating such change points and the community structures of the networks. The proposed method invokes the minimum description length principle to derive a model selection criterion, where the best estimates are defined as its minimizer. It is shown that this selection criterion yields consistent estimates for the change points as well as the community structures. For practical minimization of the selection criterion, a bottom-up search algorithm that combines the EM-algorithm with variational approximation is developed. The promising empirical properties of the proposed method are illustrated via a sequence of numerical experiments and applications to some real datasets. To the best of the authors’ knowledge, this method is one of the earliest that provides consistent estimates in the context of change point detection for time-evolving networks.