Work

Spectral CUSUM for Online Network Structure Change Detection

Year: 2023

Type: article

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.

Source: IEEE Transactions on Information Theory

DOI: 10.1109/tit.2023.3259185

Authors: Minghe Zhang, Liyan Xie, Yao Xie

Institutions: Georgia Institute of Technology, Chinese University of Hong Kong, Shenzhen

Language: English

Topic: Advanced Statistical Process Monitoring

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funders: National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation