Detection of Infections Using Graph Signal Processing in Heterogeneous Networks

Determining the causality of abnormalities in a network is the prerequisite for developing countermeasures. In this paper, we focus on infection detection in heterogeneous networks. Given a snapshot of the network which demonstrates the condition of the nodes, the goal is to distinguish between random failures and epidemic scenarios. We model the network situation as a graph signal based on the nodes' status. Detection metrics motivated by graph signal processing are introduced for the infection detection problem in hand, and an effective algorithm is proposed to solve it. Simulation results indicate a dramatic improvement in terms of detection probability compared to the current state-of-the-art.