Spectral CUSUM for Online Network Structure Change Detection
Minghe Zhang, Liyan Xie, Yao Xie

TL;DR
This paper introduces Spectral-CUSUM, an online algorithm for detecting abrupt changes in network community structures from noisy data, with proven optimality and demonstrated effectiveness in simulations and real-world seismic data.
Contribution
The paper proposes a novel spectral-based online change detection method with theoretical analysis and empirical validation for network structure changes.
Findings
Spectral-CUSUM achieves asymptotic optimality in detection delay.
The method outperforms baseline approaches in simulations.
Effective in real seismic sensor network data.
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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Taxonomy
TopicsFault Detection and Control Systems · Anomaly Detection Techniques and Applications · Complex Network Analysis Techniques
