5 papers
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)…
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…
Community detection for directed networks revisited using bimodularity
Alexandre Cionca, Chun Hei Michael Chan, Dimitri Van De Ville
Community structure is a key feature omnipresent in real-world network data. Plethora of methods have been proposed to reveal subsets of densely interconnected nodes using criteria…
Hilbert Transform on Graphs: Let There Be Phase
Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
In the past years, many signal processing operations have been successfully adapted to the graph setting. One elegant and effective approach is to exploit the eigendecomposition of…