  Detecting abrupt changes in streaming graph signals is relevant in a variety
of applications ranging from energy and water supplies, to environmental
monitoring. In this paper, we address this problem when anomalies activate
localized groups of nodes in a network. We introduce an online change-point
detection algorithm, which is fully distributed across nodes to monitor
large-scale dynamic networks. We analyze the detection statistics for
controlling the probability of a global type 1 error. Finally we illustrate the
detection and localization performance with simulated data.
