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- W4288090848 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." @default.
- W4288090848 created "2022-07-28" @default.
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- W4288090848 date "2019-10-20" @default.
- W4288090848 modified "2023-09-24" @default.
- W4288090848 title "Spectral CUSUM for Online Network Structure Change Detection" @default.
- W4288090848 doi "https://doi.org/10.48550/arxiv.1910.09083" @default.
- W4288090848 hasPublicationYear "2019" @default.
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