hypergraph-neural-networks

This paper systematically studies hypergraph node classification under label noise, proposing HyperTrust, a robust framework with HyperedgeBoost and HyperedgePrune modules, along with a unified benchmark for evaluating LLN and GLN methods on hypergraphs.

This paper studies oversmoothing in hypergraph neural networks from a dynamical-systems perspective, proposing a reaction-diffusion framework (HNRD) that preserves node-discriminative variation and achieves depth-robust propagation. Experiments show consistent improvement over baselines.