Xuan-Ting Fan

HGOOD: Hypergraph-enhanced Graph Contrastive Learning for Graph Out-of-Distribution Detection

A Hypergraph-enhanced graph contrastive learning framework for Graph Out-Of-Distribution detection (termed HGOOD), which constructs two branches to hierarchically mine graph compact semantics in a comprehensive manner and introduces a cross-branch prototype contrast that aligns the captured graph patterns with their cross-branch clustering prototypes to enhance the semantic manifold of the in-distribution graph.