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 …

We propose a sampling theory for signals that are supported on either directed or undirected
graphs. The theory follows the same paradigm as classical sampling theory. We show that …

The construction of a meaningful graph plays a crucial role in the success of many graph-
based representations and algorithms for handling structured data, especially in the …

The construction of a meaningful graph topology plays a crucial role in the effective
representation, processing, analysis, and visualization of structured data. When a natural …

Dealing with data and observations has always been an important aspect of discovery in
science. The idea that science is related to data was brilliantly summarised by Fourier in his …

This paper addresses the problem of compression of 3D point cloud sequences that are
characterized by moving 3D positions and color attributes. As temporally successive point …

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 …

Information analysis of data often boils down to properly identifying their hidden structure. In
many cases, the data structure can be described by a graph representation that supports …

Many applications collect a large number of time series, for example, the financial data of
companies quoted in a stock exchange, the health care data of all patients that visit the …

Graph neural networks aim to learn representations for graph-structured data and show
impressive performance in node classification. Recently, many methods have studied the …