3 papers
cs.LG2026
Message Passing on the Edge: Towards Scalable and Expressive GNNs
Pablo Barceló, Fabian Jogl, Alexander Kozachinskiy +3
Graph neural networks (GNNs) are widely used in graph learning and most architectures propagate information by passing messages between vertices. In this work, we shift our attenti…
cs.LG2025
Graph Representational Learning: When Does More Expressivity Hurt Generalization?
Sohir Maskey, Raffaele Paolino, Fabian Jogl +2
Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive performance remains unclear. We intr…
cs.LG2024
Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
Fabrizio Frasca, Fabian Jogl, Moshe Eliasof +4
To develop a preliminary understanding towards Graph Foundation Models, we study the extent to which pretrained Graph Neural Networks can be applied across datasets, an effort requ…