99 citations · 305 across the 23 of their papers we have counts for
6 papers · 1 filter
TwinS-GCN: Spectral conjugate for Spectral Graph Convolutional Networks
Chun Hei Michael Chan, Flavia Petruso, Dimitri Van De Ville
Graph convolutional networks propagate information by repeated local aggregation through a graph shift operator; i.e., a -layer network reaches hops neighborhood. On the one…
Graph Signal Surrogate Generation for Statistical Testing of Covariance Structure on Directed Graphs
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
Non-parametric statistical testing is based on surrogate data generation that randomizes chosen features in the empirical data. In the graph setting, graph signal processing (GSP)…
Graph Diffusion-Advection Operator for Directed Graph Signal Processing
CHM. Chan, A. Cionca, V. Škultéty +1
Graph signal processing (GSP) provides a framework for analyzing data on irregular domains, with applications in neuroscience, finance, chemistry, and social sciences. Classical GS…
Optimal Wiener-Filter Solutions for Denoising of Graph Signals on Directed Graphs
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
Graph signal processing has opened new avenues to the canonical denoising problem in interesting settings. Specifically, here we propose a Wiener-filter solution for graph signals…
Statistical Testing on Directed Graphs by Surrogate Data Generation
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
In recent years, graph signal processing has emerged as a powerful framework at the intersection of signal processing and graph theory, providing tools for the analysis of signals…
Topological Signal Processing: An Application-Oriented Tutorial
Flavia Petruso, Maria Giulia Preti, Dimitri Van De Ville
Many modern datasets are large and carry complex structural relationships. Graph-based methods have traditionally been used to represent networked data, modeling individual element…