---
title: "HGOOD: Hypergraph-enhanced Graph Contrastive Learning for Graph Out-of-Distribution Detection"
authors:
  - "Xuanting Fan"
  - "Chenyu Wang"
  - "Yueyue Gao"
  - "Wei Ju"
  - "Yifan Wang"
doi: "10.24963/ijcai.2026/285"
url: "https://ijcai.org/proceedings/2026/285"
pdf: "https://ijcai.org/proceedings/2026/0285.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "2563-2571"
track: "Main Track"
keywords:
  - "Data Mining: Anomaly/outlier detection"
  - "Data Mining: Mining graphs"
  - "Machine Learning: Self-supervised Learning"
---

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

Xuanting Fan, Chenyu Wang, Yueyue Gao, Wei Ju, Yifan Wang

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 2563-2571. DOI: [10.24963/ijcai.2026/285](https://doi.org/10.24963/ijcai.2026/285)

## 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/.