AI RESEARCH
Hypergraph Neural Diffusion: A PDE-Inspired Framework for Hypergraph Message Passing
arXiv CS.LG
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ArXi:2604.10955v1 Announce Type: new Hypergraph neural networks (HGNNs) have shown remarkable potential in modeling high-order relationships that naturally arise in many real-world data domains. However, existing HGNNs often suffer from shallow propagation, oversmoothing, and limited adaptability to complex hypergraph structures. In this paper, we propose Hypergraph Neural Diffusion (HND), a novel framework that unifies nonlinear diffusion equations with neural message passing on hypergraphs.