BHyGNN+ contrasts a hypergraph with its dual for self-supervised learning, beating supervised and self-supervised baselines on 11 benchmarks without negative samples
The work introduces BHyGNN+, a self-supervised framework for heterophilic hypergraph representation learning that contrasts augmented views of a hypergraph against its dual (with node and hyperedge roles interchanged) using cosine similarity, learning representations without ground-truth labels and without negative samples, and achieving node-classification accuracy above supervised and self-supervised baselines on eleven benchmark datasets covering heterophilic and homophilic hypergraphs plus a synthetic heterophilic dataset.