Extended Adjacency and Scale-Dependent Graph Fourier Transform via Diffusion Distances

This article proposes the augmentation of the adjacency model of networks for graph signal processing. It is assumed that no information about the network is available, apart from the initial adjacency matrix. In the proposed model, additional edges are created according to a Markov relation imposed...

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Veröffentlicht in:	IEEE transactions on signal and information processing over networks 2020, Vol.6, p.592-604
Hauptverfasser:	M. Elias, Vitor R., Martins, Wallace A., Werner, Stefan
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomalies Diffusion Diffusion distances diffusion maps Distance measurement extended adjacency Fourier transforms graph signal processing Laplace equations Markov processes Nodes scale-dependent graph Fourier transform Sensors Signal processing Symmetric matrices
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