hypergraph-learning

This paper proposes a Multi-Granularity Hypergraph Representation Learning (MGHRL) framework that adaptively generates hyperedges at multiple granularities using granular-ball splitting to capture high-order relationships in graphs, outperforming baseline models on benchmark datasets.

The paper introduces Selective Hypergraph Refinement (SHR), a label-free post-processing method that uses hypergraphs to refine cluster assignments in frozen graph models without updating parameters, showing measurable improvements.

This paper proposes ChemHyperMag, a physics-informed magnetic hypergraph learning method for multitask ADMET prediction that uses functional group hypergraphs and a Hermitian magnetic Laplacian to capture asymmetric interactions and directional signals, improving prediction accuracy with fewer labeled samples.