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.
Xuan-Ting Fan, Chenyu Wang, Yue-Yue Gao et al.
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