Reference : Diffusion-based Virtual Graph Adjacency for Fourier Analysis of Network Signals
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/45299
Diffusion-based Virtual Graph Adjacency for Fourier Analysis of Network Signals
English
Elias, Vitor R. M. mailto []
Alves Martins, Wallace mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Werner, Stefan mailto []
Nov-2020
XXXVIII SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES E PROCESSAMENTO DE SINAIS, Florianópolis 22-25 November 2020
Yes
National
XXXVIII SIMPÓSIO BRASILEIRO DE TELECOMUNICAÇÕES E PROCESSAMENTO DE SINAIS
from 22-11-2020 to 25-11-2020
Brazil
[en] diffusion distances ; virtual adjacency matrix ; graph signal processing ; graph Fourier transform
[en] This work proposes a graph model for networks where node collaborations can be described by the Markov property. The proposed model augments an initial graph adjacency using diffusion distances. The resulting virtual adjacency depends on a diffusion-scale parameter, which leads to a controlled shift in the graph-Fourier-transform spectrum. This enables a frequency analysis tailored to the actual network collaboration, revealing more information on the graph signal when compared to traditional approaches. The proposed model is employed for anomaly detection in real and synthetic networks, and results confirm that using the proposed virtual adjacency yields better classification than the initial adjacency.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/45299
H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems

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