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.

翻译：从带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。本文提出了一种名为Spectral-CUSUM的在线变化检测算法，通过广义似然比统计量来检测未知的网络结构变化。我们刻画了Spectral-CUSUM过程的平均运行长度和期望检测延迟，并证明了其渐近最优性。最后，我们通过模拟实验和基于传感器网络数据的地震事件检测实际案例，展示了Spectral-CUSUM过程的良好性能，并与多种基线方法进行了比较。