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 …

The effective representation, processing, analysis, and visualization of large-scale structured
data, especially those related to complex domains, such as networks and graphs, are one of …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

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 …

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 …

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 …

An intuitive and accessible text explaining the fundamentals and applications of graph
signal processing. Requiring only an elementary understanding of linear algebra, it covers …

The construction of a meaningful graph topology plays a crucial role in the effective
representation, processing, analysis, and visualization of structured data. When a natural …

Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear processing of
graph signals, exhibiting success in recommender systems, power outage prediction, and …

Graphs are fundamental mathematical structures used in various fields to represent data,
signals, and processes. In this paper, we propose a novel framework for learning/estimating …