Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

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 We present the Temporal Graph Benchmark (TGB), a collection of challenging and
diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine …

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

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 …

Despite their widespread success in various domains, Transformer networks have yet to
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …

Link prediction is a very fundamental task on graphs. Inspired by traditional path-based
methods, in this paper we propose a general and flexible representation learning framework …