Self-Supervised Hypergraph Learning with Substructure Awareness for Hyperedge Prediction

Hyperedge prediction plays a central role in hypergraph learning, enabling the inference of high-order relations among multiple entities. However, existing methods often rely on a simplistic flat set assumption, treating candidate hyperedges as unstructured collections of nodes and neglecting their potential internal compositionality. Furthermore, the severe scarcity of observed hyperedges poses a challenge for effective supervision. In this work, we propose S3Hyper, a Substructure-contextualized Self-Supervised framework for Hyperedge prediction, which jointly addresses these two challenges. Specifically, we design a substructure-contextualized hyperedge aggregator that models the internal hierarchy of candidate hyperedges by leveraging sub-hyperedge information. In parallel, we introduce an adaptive tri-directional contrastive learning module that incorporates node-level, hyperedge-level, and cross-level alignment objectives, supported by temperature-adaptive mechanisms. Experimental results on four public datasets demonstrate that S3Hyper consistently outperforms strong baselines, with ablation studies verifying the effectiveness of each component.

Authors

• Ming Li
• Huiting Wang
• Yuting Chen Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong
• Lu Bai School of Artificial Intelligence, and Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing Normal University
• Lixin Cui School of Information, Central University of Finance and Economics, Beijing, China
• Feilong Cao
• Ke Lv School of Engineering Science, University of Chinese Academy of Sciences Peng Cheng Laboratory

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