  We propose an online detection procedure for cascading failures in the
network from sequential data, which can be modeled as multiple correlated
change-points happening during a short period. We consider a temporal diffusion
network model to capture the temporal dynamic structure of multiple
change-points and develop a sequential Shewhart procedure based on the
generalized likelihood ratio statistics based on the diffusion network model
assuming unknown post-change distribution parameters. We also tackle the
computational complexity posed by the unknown propagation. Numerical
experiments demonstrate the good performance for detecting cascade failures.
