We propose an interpretable graph neural network framework to denoise single or multiple
noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to …

A key challenge in the field of graph signal processing is to design proper transform
methods to extract valuable information from signals on weighted graphs. This paper first …

We propose an interpretable graph neural network framework to denoise single or multiple
noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to …

In the past decade, significant progress has been made to generalize classical tools from
Fourier analysis to analyze and process signals defined on networks. In this paper, we …

In graph signal processing, the underlying network in many studies is assumed to be
undirected. Although the directed graph model is rarely adopted, it is more appropriate for …

Vertex-frequency analysis, particularly the windowed graph Fourier transform (WGFT), is a
significant challenge in graph signal processing. Tight frame theories is known for its low …

The theory of discrete frames was introduced in the 1950s by Duffin and Schaeffer, when
they initiated a systematic study of dictionaries for the efficient and robust representation of …

Network processes are becoming increasingly ubiquitous, with examples ranging from the
measurements of neural activities at different regions of the brain to infectious states of …