RADE: Random Add-Drop Edge as a Regularizer
Graph Neural Networks (GNNs) suffer from overfitting and over-squashing of long-range information. Stochastic graph augmentations (e.g., edge deletion) regularize training against overfitting but can introduce train-inference misalignment and do not improve over-squashing. In contrast, rewiring methods improve connectivity to mitigate over-squashing, but are not designed to regularize training. We propose Random Add-Drop Edge (RADE), a stochastic graph augmentation method that jointly drops and adds edges to address both overfitting and over-squashing simultaneously. RADE is provably designed to align training and inference so that random augmentations regularize training without distribu...
| Authors | Danial Saber, Amirali Salehi-Abari |
|---|---|
| Primary category | cs.LG |
| Published | 30 May 2026 |
| Last updated at source | 2 June 2026 |
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