2017 conference paper
Detection of infections using graph signal processing in heterogeneous networks
Globecom 2017 - 2017 ieee global communications conference.
TL;DR:
This paper focuses on infection detection in heterogeneous networks and model the network situation as a graph signal based on the nodes' status, which helps distinguish between random failures and epidemic scenarios.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018
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.