  We consider the problem of sequential graph topology change-point detection
from graph signals. We assume that signals on the nodes of the graph are
regularized by the underlying graph structure via a graph filtering model,
which we then leverage to distill the graph topology change-point detection
problem to a subspace detection problem. We demonstrate how prior information
on the spectral signature of the post-change graph can be incorporated to
implicitly denoise the observed sequential data, thus leading to a natural
CUSUM-based algorithm for change-point detection. Numerical experiments
illustrate the performance of our proposed approach, particularly underscoring
the benefits of (potentially noisy) prior information.
