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arXiv · 1910.09083

Spectral CUSUM for Online Network Structure Change Detection

Minghe Zhang·Liyan Xie·Yao Xie
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Abstract

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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Minghe Zhang, Liyan Xie, Yao Xie. 2019-10-20. Spectral CUSUM for Online Network Structure Change Detection. https://arxiv.org/abs/1910.09083

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