Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …

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 …

Effectively predicting molecular interactions has the potential to accelerate molecular
dynamics by multiple orders of magnitude and thus revolutionize chemical simulations …

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 …

In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …

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 …