The goal of this paper is to propose novel strategies for adaptive learning of signals defined
over graphs, which are observed over a (randomly) time-varying subset of vertices. We …

This paper proposes efficient batch-based and online strategies for kernel regression over
graphs (KRG). The proposed algorithms do not require the input signal to be a graph signal …

This work proposes a normalized least-mean-squares (NLMS) algorithm for online
estimation of bandlimited graph signals (GS) using a reduced number of noisy …

This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces.
We consider both centralized and fully distributed implementations. We first define nonlinear …

As irregularly structured data representations, graphs have received a large amount of
attention in recent years and have been widely applied to various real-world scenarios such …

The aim of this chapter is to give an overview of the recent advances related to sampling and
recovery of signals defined over graphs. First, we illustrate the conditions for perfect recovery …

In this article, we are interested in adaptive and distributed estimation of graph filters from
streaming data. We formulate this problem as a consensus estimation problem over graphs …

Most works on graph signal processing assume static graph signals, which is a limitation
even in comparison to traditional DSP techniques where signals are modeled as sequences …

Efficient and robust online processing techniques for irregularly structured data are crucial in
the current era of data abundance. In this paper, we propose a graph/network version of the …

The aim of this work is to develop a fully-distributed algorithmic framework for training graph
convolutional networks (GCNs). The proposed method is able to exploit the meaningful …