Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

The state-of-the-art graph wavelet decomposition was constructed by maximum spanning
tree (MST)-based downsampling and two-channel graph wavelet filter banks. In this work …

Modern data analytics applications on graphs often operate on domains where graph
topology is not known a priori, and hence its determination becomes part of the problem …

Graph signal processing deals with signals whose domain, defined by a graph, is irregular.
An overview of basic graph forms and definitions is presented first. Spectral analysis of …

This paper presents a graph signal denoising method with the trilateral filter defined in the
graph spectral domain. The original trilateral filter (TF) is a data-dependent filter that is …

In this brief, the minimax design problem of graph filter using Chebyshev polynomial
approximation (CPA) is studied. First, conventional CPA graph filter design is investigated to …

This paper builds upon the basic theory of multirate systems for graph signals developed in
the companion paper (Part I) and studies M-channel polynomial filter banks on graphs. The …

Signal processing on graphs finds applications in many areas. In recent years, renewed
interest on this topic was kindled by two groups of researchers. Narang and Ortega …

We describe a method of oversampling signals defined on a weighted graph by using an
oversampled graph Laplacian matrix. The conventional method of using critically sampled …