Dynamic change-point detection using similarity networks
This work addresses change-point detection in network data for applications like sensor monitoring, but it appears incremental as it builds on existing similarity network methods.
The paper tackles the problem of detecting a change-point in sequences of similarity networks, where a subset of nodes becomes anomalous after the change, and proposes a simple sequential procedure based on node-wise average similarity measures, showing it has reasonably good performance in simulations and real-data examples.
From a sequence of similarity networks, with edges representing certain similarity measures between nodes, we are interested in detecting a change-point which changes the statistical property of the networks. After the change, a subset of anomalous nodes which compares dissimilarly with the normal nodes. We study a simple sequential change detection procedure based on node-wise average similarity measures, and study its theoretical property. Simulation and real-data examples demonstrate such a simply stopping procedure has reasonably good performance. We further discuss the faulty sensor isolation (estimating anomalous nodes) using community detection.