3 papers
cs.LG2025
Generalizable Insights for Graph Transformers in Theory and Practice
Timo Stoll, Luis Müller, Christopher Morris
Graph Transformers (GTs) have shown strong empirical performance, yet current architectures vary widely in their use of attention mechanisms, positional embeddings (PEs), and expre…
cs.LG2025
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca +9
While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and rele…
cs.LG2024
Towards Principled Graph Transformers
Luis Müller, Daniel Kusuma, Blai Bonet +1
Graph learning architectures based on the k-dimensional Weisfeiler-Leman (k-WL) hierarchy offer a theoretically well-understood expressive power. However, such architectures often…