Are we measuring oversmoothing in graph neural networks correctly?
International Conference on Learning Representations (ICLR), 2026
Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drops sharply. Traditionally, oversmoothing has been quantified using metrics that measure the similarity of neighbouring node features, such as …
Stuart-Landau Oscillatory Graph Neural Network
The Web Conference (WWW), 2026
Oscillatory Graph Neural Networks (OGNNs) are an emerging class of physics-inspired architectures designed to mitigate oversmoothing and vanishing gradient problems in deep GNNs. In this work, we introduce the Complex-Valued Stuart-Landau Graph Neural Network (SLGNN), a novel architecture grounded in Stuart-Landau …
A Low-Rank Perspective on Oversmoothing in Graph Neural Networks
AAAI Conference on Artificial Intelligence (AAAI), Workshop on Connecting Low-Rank Representations in AI, 2025
Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drops sharply. Traditionally, oversmoothing has been quantified using metrics that measure the similarity of neighbouring node features, such as …