Bhygnn+: unsupervised representation learning for heterophilic hypergraphs

Hypergraph neural networks (HyGNNs) have demonstrated remarkable success in modeling higher-order relationships among entities. However, their performance often degrades on heterophilic hypergraphs, where nodes connected by the same hyperedge tend to have dissimilar semantic representations or belon