Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Abstract Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on
graph signal filters. Some models able to learn arbitrary spectral filters have emerged …

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 …

We show that standard Transformers without graph-specific modifications can lead to
promising results in graph learning both in theory and practice. Given a graph, we simply …

Attention layers, as commonly used in transformers, form the backbone of modern deep
learning, yet there is no mathematical description of their benefits and deficiencies as …

Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which
model graphs as collections of subgraphs. So far, the design space of possible Subgraph …

We consider representation learning on periodic graphs encoding crystal materials. Different
from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on a …

Designing expressive Graph Neural Networks (GNNs) is a central topic in learning graph-
structured data. While numerous approaches have been proposed to improve GNNs in …

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …