{"type":"ScholarlyArticle","title":"HGOOD: Hypergraph-enhanced Graph Contrastive Learning for Graph Out-of-Distribution Detection","authors":[{"name":"Xuanting Fan","slug":"xuanting-fan","given":"Xuanting","family":"Fan","affiliation":""},{"name":"Chenyu Wang","slug":"chenyu-wang","given":"Chenyu","family":"Wang","affiliation":""},{"name":"Yueyue Gao","slug":"yueyue-gao","given":"Yueyue","family":"Gao","affiliation":""},{"name":"Wei Ju","slug":"wei-ju","given":"Wei","family":"Ju","affiliation":""},{"name":"Yifan Wang","slug":"yifan-wang","given":"Yifan","family":"Wang","affiliation":""}],"abstract":"With the increasing application of graph learning advanced by deep learning, out-of-distribution (OOD) detection for graph-structured data has become an imperative challenge in the real world. Graph neural networks (GNNs) provide a promising solution for OOD detection. However, GNNs' core message-passing mechanism inherently relies on local neighborhood aggregation, and traditional graph structures only characterize pairwise node relations, failing to capture high-order associations and global patterns. Towards this end, in this paper, we propose a Hypergraph-enhanced graph contrastive learning framework for Graph Out-Of Distribution detection (termed HGOOD). Specifically, we construct two branches to mine graph semantics in a comprehensive manner. On the one hand, we employ a graph feature branch to encode neighborhood interactions via node-level and graph-level contrastive learning. On the other hand, we incorporate the hypergraph-global branch, which adaptively models graphs’ high-order global correlations. Building upon this, we introduce 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 (ID) graph. Extensive experiments on benchmark datasets demonstrate that our HGOOD consistently outperforms prior methods. Our code is available at https://anonymous.4open.science/r/HGOOD-CFE2/.","doi":"10.24963/ijcai.2026/285","url":"https://ijcai.org/proceedings/2026/285","pdf":"https://ijcai.org/proceedings/2026/0285.pdf","markdown":"https://ijcai.org/proceedings/2026/285.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/285","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":285,"pages":{"first":2563,"last":2571},"keywords":["Data Mining: Anomaly/outlier detection","Data Mining: Mining graphs","Machine Learning: Self-supervised Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}