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 …

We study the optimal design of graph filters (GFs) to implement arbitrary linear
transformations between graph signals. GFs can be represented by matrix polynomials of …

This paper builds theoretical foundations for the recovery of a newly proposed class of
smooth graph signals, approximately bandlimited graph signals, under three sampling …

We consider the problem of recovering a smooth graph signal from noisy samples taken on
a subset of graph nodes. The smoothness of the graph signal is quantified in terms of total …

The aim of this paper is to propose distributed strategies for adaptive learning of signals
defined over graphs. Assuming the graph signal to be bandlimited, the method enables …

In this paper, we consider nonsubsampled graph filter banks (NSGFBs) to process data on a
sparse graph. The analysis filter banks of NSGFBs have small bandwidth, pass/block the …

Signals and information related to networks can be modeled and processed as graph
signals. It has been shown that if a graph signal is smooth enough to satisfy certain …

The recovery of missing samples from available noisy measurements is a fundamental
problem in signal processing. This process is also sometimes known as graph signal …

Polynomial graph filters and their inverses play important roles in graph signal processing.
In this paper, we introduce the concept of multiple commutative graph shifts and polynomial …

A signal in a network (graph) can be defined as a vector whose elements represent the
value of a given magnitude at the different nodes. A linear network (graph) operator is then a …