Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation
This addresses change-point detection in real-time graph data streams, which is incremental as it builds on existing methods with a novel approach.
The paper tackles the problem of detecting change-points and localizing affected nodes in graph-based data streams by proposing a kernel-based method that estimates likelihood-ratios with graph smoothness assumptions, achieving results demonstrated through extensive synthetic experiments.
Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point $τ$, a change occurs at a subset of nodes $C$, which affects the probability distribution of their associated node streams. In this paper, we propose a novel kernel-based method to both detect $τ$ and localize $C$, based on the direct estimation of the likelihood-ratio between the post-change and the pre-change distributions of the node streams. Our main working hypothesis is the smoothness of the likelihood-ratio estimates over the graph, i.e connected nodes are expected to have similar likelihood-ratios. The quality of the proposed method is demonstrated on extensive experiments on synthetic scenarios.