Spectral CUSUM for Online Network Structure Change Detection
This addresses the need for online change detection in network structures, such as in sensor networks for seismic monitoring, but is incremental as it builds on existing CUSUM methods.
The paper tackled the problem of detecting abrupt changes in network community structure from noisy observations by proposing the Spectral-CUSUM algorithm, which achieved asymptotic optimality with characterized average run length and expected detection delay, and demonstrated good performance in simulations and real seismic event detection 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.