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 …

Network topology inference is a significant problem in network science. Most graph signal
processing (GSP) efforts to date assume that the underlying network is known and then …

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals
that live naturally on irregular data kernels described by graphs (eg, social networks …

Modern neuroimaging techniques provide us with unique views on brain structure and
function; ie, how the brain is wired, and where and when activity takes place. Data acquired …

We address the problem of identifying the structure of an undirected graph from the
observation of signals defined on its nodes. Fundamentally, the unknown graph encodes …

Abstract Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. So far, these neural …

Graph processes exhibit a temporal structure determined by the sequence index and and a
spatial structure determined by the graph support. To learn from graph processes, an …

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 …

Stationarity is a cornerstone property that facilitates the analysis and processing of random
signals in the time domain. Although time-varying signals are abundant in nature, in many …

Signal processing (SP) excels at analyzing, processing, and inferring information defined
over regular (first continuous, later discrete) domains such as time or space. Indeed, the last …