Reaction–Diffusion Framework Mitigates Oversmoothing in Hypergraph Neural Networks
A new preprint presents a reaction–diffusion framework to address oversmoothing in hypergraph neural networks (HGNNs), where deep propagation can cause loss of discriminative features. The proposed Hypergraph Neural Reaction–Diffusion (HNRD) model introduces a reaction mechanism to counteract diffusion-induced dissipation, stabilizing node representations even in deep architectures. Experimental results show that HNRD consistently outperforms existing hypergraph baselines and maintains robust performance under deep propagation and perturbations.
Why it matters: This work offers a principled approach for building deeper and more robust hypergraph neural networks, potentially expanding their practical use in complex relational data settings.
Full story at: arXiv Machine Learning ↗