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cs.LG2026

Are We Measuring Oversmoothing in Graph Neural Networks Correctly?

Kaicheng Zhang, Piero Deidda, Desmond Higham +1

Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as the number of layers increases, node embeddings become increasingly similar, and model performance drop…

cs.LG2026

Stuart-Landau Oscillatory Graph Neural Network

Kaicheng Zhang, David N. Reynolds, Piero Deidda +1

Oscillatory Graph Neural Networks (OGNNs) are an emerging class of physics-inspired architectures designed to mitigate oversmoothing and vanishing gradient problems in deep GNNs. I…

cs.LG2026

Provable Emergence of Deep Neural Collapse and Low-Rank Bias in -Regularized Nonlinear Networks

Emanuele Zangrando, Piero Deidda, Simone Brugiapaglia +2

We present a unified theoretical framework connecting the first property of Deep Neural Collapse (DNC1) to the emergence of implicit low-rank bias in nonlinear networks trained wit…