  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.
