2 citations · 4 across the 3 of their papers we have counts for
5 papers
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…
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…
Aligning Transformers with Weisfeiler-Leman
Luis Müller, Christopher Morris
Graph neural network architectures aligned with the -dimensional Weisfeiler--Leman (-WL) hierarchy offer theoretically well-understood expressive power. However, these archit…
: A Parameter-Efficient Foundation Model for Molecular Learning
Kerstin Kläser, Błażej Banaszewski, Samuel Maddrell-Mander +5
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data a…
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…