Graphs are flexible mathematical objects that can represent many entities and knowledge
from different domains, including in the life sciences. Graph neural networks (GNNs) are …

Graph plays a significant role in representing and analyzing complex relationships in real-
world applications such as citation networks, social networks, and biological data. Recently …

Learning on Graphs has attracted immense attention due to its wide real-world applications.
The most popular pipeline for learning on graphs with textual node attributes primarily relies …

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 …

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

Message passing neural networks (MPNNs) have been shown to suffer from the
phenomenon of over-squashing that causes poor performance for tasks relying on long …

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 …

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 …

Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …

Message passing graph neural networks (GNNs) are a popular learning architectures for
graph-structured data. However, one problem GNNs experience is oversquashing, where a …