Journal
IEEE TRANSACTIONS ON INFORMATION THEORY
Volume 69, Issue 7, Pages 4691-4707Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIT.2023.3259185
Keywords
Ihange-point detection; false-alarm control; graph community change detection; spectral method
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This paper proposes an online change detection algorithm called Spectral-CUSUM to detect unknown network structure changes using a generalized likelihood ratio statistic. The average run length (ARL) and the expected detection delay (EDD) of the Spectral-CUSUM procedure are characterized and its asymptotic optimality is proven. Finally, the good performance of the Spectral-CUSUM procedure is demonstrated and compared with several baseline methods on seismic event detection using sensor network data.
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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