Spectral CUSUM for Online Network Structure Change Detection
Statistics Theory
2023-03-17 v8 Machine Learning
Social and Information Networks
Statistics Theory
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.
Cite
@article{arxiv.1910.09083,
title = {Spectral CUSUM for Online Network Structure Change Detection},
author = {Minghe Zhang and Liyan Xie and Yao Xie},
journal= {arXiv preprint arXiv:1910.09083},
year = {2023}
}
Comments
Accepted by IEEE Transactions on Information Theory