One of the key challenges in the area of signal processing on graphs is to design transforms and dictionary methods to identify and exploit structure in signals on weighted graphs. In this paper, we first generalize graph Fourier transform (GFT) to spectral graph fractional Fourier transform (SGFRFT), which is then used to define a novel transform named spectral graph fractional wavelet transform (SGFRWT), which is a generalized and extended version of spectral graph wavelet transform (SGWT). A fast algorithm for SGFRWT is also derived and implemented based on Fourier series approximation. Some potential applications of SGFRWT are also presented
International audienceWe propose a new point of view in the study of Fourier analysis on graphs, tak...
Classical wavelet, wavelet packets and time-frequency dictionaries have been generalized to the grap...
International audienceBasic operations in graph signal processing consist in processing signals inde...
International audienceOne of the key challenges in the area of signal processing on graphs is to des...
In graph signal processing, many studies assume that the underlying network is undirected. Although ...
We propose a novel method for constructing wavelet transforms of functions defined on the vertices o...
AbstractWe propose a novel method for constructing wavelet transforms of functions defined on the ve...
Nowadays graphs became of significant importance given their use to describe complex system dynamics...
Graph-structured data appears in many modern applications like social networks, sensor networks, tra...
International audienceRecent progress in graph signal processing (GSP) has addressed a number of pro...
In this article, a new family of graph wavelets, abbreviated LocLets for Localized graph waveLets, i...
To analyze and synthesize signals on networks or graphs, Fourier theory has been extended to irregul...
The graph Laplacian is widely used in the graph signal processing field. When attempting to design g...
One of the key challenges in the area of signal processing on graphs is to design dictionaries and t...
International audienceWe propose a new point of view in the study of Fourier analysis on graphs, tak...
Classical wavelet, wavelet packets and time-frequency dictionaries have been generalized to the grap...
International audienceBasic operations in graph signal processing consist in processing signals inde...
International audienceOne of the key challenges in the area of signal processing on graphs is to des...
In graph signal processing, many studies assume that the underlying network is undirected. Although ...
We propose a novel method for constructing wavelet transforms of functions defined on the vertices o...
AbstractWe propose a novel method for constructing wavelet transforms of functions defined on the ve...
Nowadays graphs became of significant importance given their use to describe complex system dynamics...
Graph-structured data appears in many modern applications like social networks, sensor networks, tra...
International audienceRecent progress in graph signal processing (GSP) has addressed a number of pro...
In this article, a new family of graph wavelets, abbreviated LocLets for Localized graph waveLets, i...
To analyze and synthesize signals on networks or graphs, Fourier theory has been extended to irregul...
The graph Laplacian is widely used in the graph signal processing field. When attempting to design g...
One of the key challenges in the area of signal processing on graphs is to design dictionaries and t...
International audienceWe propose a new point of view in the study of Fourier analysis on graphs, tak...
Classical wavelet, wavelet packets and time-frequency dictionaries have been generalized to the grap...
International audienceBasic operations in graph signal processing consist in processing signals inde...