Non-Parametric Community Change-Points Detection In Streaming Graph Signals

Detecting changes in network-structured time series data is of utmost importance in critical applications as diverse as detecting denial of service attacks against online service providers or monitoring energy and water supplies. The aim of this paper is to address this challenge when anomalies activate unknown groups of nodes in a network. We devise an online change-point detection algorithm that fully benefits from the recent advances in graph signal processing to exploit the characteristics of the data that lie on irregular supports. Built upon the kernel machinery, it performs density ratio estimation in an online way. The algorithm is scalable in the sense that it is spatially distributed over the nodes to monitor large-scale dynamic networks. The detection and localization performances of the algorithm are illustrated with simulated data.