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 …

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 …

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-
art accuracy on recommender system (RecSys) benchmarks. However, recommendation …

This tutorial serves as a comprehensive guide for understanding graph databases, focusing
on the fundamentals of graph theory while showcasing practical applications across various …

This paper develops new designs for recommendation systems inspired by recent advances
in graph signal processing. Recommendation systems aim to predict unknown ratings by …

We consider the problem of designing sparse sampling strategies for multidomain signals,
which can be represented using tensors that admit a known multilinear decomposition. We …

In the context of recommender systems, collaborative filtering is the method of predicting the
ratings of a set of items given by a set of users based on partial knowledge of the ratings …

Recently, 3D Gaussian Splatting, as a novel 3D representation, has garnered attention for its
fast rendering speed and high rendering quality. However, this comes with high memory …

In this paper, we consider the problem of subsampling and reconstruction of signals that
reside on the vertices of a product graph, such as sensor network time series, genomic …

Graph Signal Processing (GSP) based recommendation algorithms have recently attracted
lots of attention due to its high efficiency. However, these methods failed to consider the …