2 papers
cs.LG2025
Boosting Graph Neural Network Expressivity with Learnable Lanczos Constraints
Niloofar Azizi, Nils Kriege, Horst Bischof
Graph Neural Networks (GNNs) excel in handling graph-structured data but often underperform in link prediction tasks compared to classical methods, mainly due to the limitations of…
cs.LG2024
Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph
Andreas Roth, Franka Bause, Nils M. Kriege +1
The ability of message-passing neural networks (MPNNs) to fit complex functions over graphs is limited as most graph convolutions amplify the same signal across all feature channel…