3 papers
cs.LG2026
Can Graph Foundation Models Generalize Over Architecture?
Benjamin Gutteridge, Michael Bronstein, Xiaowen Dong
Graph foundation models (GFMs) have recently attracted interest due to the promise of graph neural network (GNN) architectures that generalize zero-shot across graphs of arbitrary…
cs.LG2025
On Measuring Long-Range Interactions in Graph Neural Networks
Jacob Bamberger, Benjamin Gutteridge, Scott le Roux +2
Long-range graph tasks -- those dependent on interactions between distant nodes -- are an open problem in graph neural network research. Real-world benchmark tasks, especially the…
cs.LG2025
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
Álvaro Arroyo, Alessio Gravina, Benjamin Gutteridge +5
Graph Neural Networks (GNNs) are models that leverage the graph structure to transmit information between nodes, typically through the message-passing operation. While widely succe…