Recently, Transformer model, which has achieved great success in many artificial
intelligence fields, has demonstrated its great potential in modeling graph-structured data …

Large‐scale artificial intelligence (AI) models such as ChatGPT have the potential to
improve performance on many benchmarks and real‐world tasks. However, it is difficult to …

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 …

Abstract Message Passing Neural Networks (MPNNs) are instances of Graph Neural
Networks that leverage the graph to send messages over the edges. This inductive bias …

Abstract Graph Neural Networks (GNNs) that are based on the message passing (MP)
paradigm generally exchange information between 1-hop neighbors to build node …

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

Transformers for graph data are increasingly widely studied and successful in numerous
learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous …

Graph transformers have emerged as a promising architecture for a variety of graph learning
and representation tasks. Despite their successes, though, it remains challenging to scale …

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 …

Abstract Graph Neural Networks (GNNs) have shown great potential in the field of graph
representation learning. Standard GNNs define a local message-passing mechanism which …