Graph-based techniques emerged as a choice to deal with the dimensionality issues in
modeling multivariate time series. However, there is yet no complete understanding of how …

Dynamic systems of graph signals are encountered in various applications, including social
networks, power grids, and transportation. While such systems can often be described as …

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

Data that house topological information is manifested as relationships between multiple
variables via a graph formulation. Various methods have been developed for analyzing time …

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 …

Graph convolutional networks (GCNs) have become one of the important technologies for
solving graph structured data problems. GCNs utilize convolutional networks to learn node …

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 …

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 …

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 …

Vertex-domain and temporal-domain smoothness of time-varying graph signals are cardinal
properties that can be exploited for effective graph signal reconstruction from limited …