Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer
with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph …

The Transformer architecture has become a dominant choice in many domains, such as
natural language processing and computer vision. Yet, it has not achieved competitive …

Predicting the binding structure of a small molecule ligand to a protein--a task known as
molecular docking--is critical to drug design. Recent deep learning methods that treat …

In recent years, the Transformer architecture has proven to be very successful in sequence
processing, but its application to other data structures, such as graphs, has remained limited …

The Transformer architecture has gained growing attention in graph representation learning
recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by …

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 …

Graph neural networks are powerful architectures for structured datasets. However, current
methods struggle to represent long-range dependencies. Scaling the depth or width of …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …