A fundamental problem in signal processing is to denoise a signal. While there are many
well-performing methods for denoising signals defined on regular domains, including …

Hypergraph Neural networks (HyperGNNs) and hypergraph signal denoising (HyperGSD)
are two fundamental topics in higher-order network modeling. Understanding the connection …

Graph neural networks (GNNs) have emerged as a promising solution to deal with
unstructured data, outperforming traditional deep learning architectures. However, most of …

We develop a novel convolutional architecture tailored for learning from data defined over
directed acyclic graphs (DAGs). DAGs can be used to model causal relationships among …

Graph Neural Networks (GNNs) have emerged as a notorious alternative to address
learning problems dealing with non-Euclidean datasets. However, although most works …

Graph neural networks (GNNs) have been successfully employed in a myriad of applications
involving graph signals. Theoretical findings establish that GNNs use nonlinear activation …