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 …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

Abstract Self-Supervised Learning (SSL) surmises that inputs and pairwise positive
relationships are enough to learn meaningful representations. Although SSL has recently …

In this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …

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 …

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 …

Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional …

In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on
higher-order networks. Drawing analogies from discrete and graph signal processing, we …