Spectral CUSUM for Online Network Structure Change Detection

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.