Hypergraph Contrastive Learning

Hypergraph Contrastive Learning is a self-supervised learning method based on hypergraph structures, designed to capture higher-order relationships and complex structural information through a contrastive learning framework, thereby enhancing the model's performance in tasks such as node representation learning, clustering, and classification. This method improves data representation capabilities by constructing hypergraphs, further optimizing feature embeddings, and thus playing a significant role in various graph data application scenarios.