  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 $\tau$, 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 $\tau$ 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.
