Hi 👋, I’m Luis, a second year PhD student at RWTH Aachen University under the supervision of Christopher Morris.
I’m interested in studying the capabilities and limitations of general-purpose machine learning architectures in the context of graph learning. My current research focus is on deriving a principled understanding of graph transformers and their potential benefits over GNNs. Email:email@example.com.
Towards Principled Graph Transformers
Luis Müller, Daniel Kusuma, Christopher Morris
We show that the Edge Transformer, a model originally proposed for improved systematic generalization over standard transformers, has provable 3-WL expressivity. We then demonstrate through a range of experiments on expressivity, molecular prediction and neural algorithmic reasoning benchmarks that the Edge Transformer matches or improves over SOTA graph learning models in terms of predictive performance.
Attending to Graph Transformers
Luis Müller, Michael Galkin, Christopher Morris, Ladislav Rampasek
We propose a taxonomy of graph transformers, overview their theoretical properties and investigate experimentally how well graph transformers can recover graph structure and mitigate issues with over-smoothing and over-squashing. Accepted at TMLR.