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 …

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 …

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 …

This article presents a review of recent advances in neuroscience research in the specific
area of brain connectivity as a potential biomarker of Alzheimer's disease with a focus on the …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

In this paper we study the problem of content-based image retrieval. In this problem, the
most popular performance measure is the top precision measure, and the most important …

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in
designing various brain-computer interface (BCI) applications. Most of the current …

We investigate the problem of learning an undirected, weighted bipartite graph under the
Gaussian Markov random field model, for which we present an optimization formulation …

The application of graph theory to model the complex structure and function of the brain has
shed new light on its organization, prompting the emergence of network neuroscience …

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI)
signals is important in understanding neural representation and information processing in …