Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolving in time, such as spreading or propagation of waves on a network. The analysis of this type of data requires a new set of methods that takes into account the time and graph dimensions. We propose a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex evolution follows a dynamic process. We demonstrate that this set of functions can be combined with sparsity based approaches such as compressive sensing to reveal information on the dynamic processes occurring on a graph. Experiments on real seismological data show the efficiency of the technique, allowing to estimate the epicenter of earthquake events recorded by a seismic network.

Tracking time-vertex propagation using dynamic graph wavelets / Grassi, Francesco; Perraudin, Nathanael; Ricaud, Benjamin. - ELETTRONICO. - (2017), pp. 351-355. ((Intervento presentato al convegno 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP) tenutosi a Washington DC, DC, USA, USA nel 7-9 Dec. 2016 [10.1109/GlobalSIP.2016.7905862].

Tracking time-vertex propagation using dynamic graph wavelets

GRASSI, FRANCESCO;
2017

Abstract

Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolving in time, such as spreading or propagation of waves on a network. The analysis of this type of data requires a new set of methods that takes into account the time and graph dimensions. We propose a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex evolution follows a dynamic process. We demonstrate that this set of functions can be combined with sparsity based approaches such as compressive sensing to reveal information on the dynamic processes occurring on a graph. Experiments on real seismological data show the efficiency of the technique, allowing to estimate the epicenter of earthquake events recorded by a seismic network.
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Descrizione: Articolo su proceedings GlobalSIP 2016
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2669773
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