基于谱CUSUM的网络结构变化在线检测
Spectral CUSUM for Online Network Structure Change Detection
摘要：
从噪声观测中检测网络社区结构的突变是统计学和机器学习中的一个基本问题。本文提出了一种在线变化检测算法Spectral-Rumium，通过广义似然比统计量来检测未知的网络结构变化。我们的平均游程长度（ARL）和期望检测延迟（EDD）的频谱-SPECTRAL-SPECTRUM过程的特点，并证明其渐近最优性。最后，我们展示了良好的性能的频谱-SPECTUM程序，并比较它与几个基线方法使用模拟和实际数据的例子，地震事件检测传感器网络数据。
英文摘要：
Detecting abrupt changes in the community structure of a network from noisy observations is a fundamental problem in statistics and machine learning. This paper presents an online change detection algorithm called Spectral-CUSUM to detect unknown network structure changes through a generalized likelihood ratio statistic. We characterize the average run length (ARL) and the expected detection delay (EDD) of the Spectral-CUSUM procedure and prove its asymptotic optimality. Finally, we demonstrate the good performance of the Spectral-CUSUM procedure and compare it with several baseline methods using simulations and real data examples on seismic event detection using sensor network data.
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