Enhancing Hyperedge Prediction With Context-Aware Self-Supervised Learning
Yunyong Ko, Hanghang Tong, Sang‐Wook Kim · IEEE Transactions on Knowledge and Data Engineering · 2025
Hypergraphs can naturally modelgroup-wise relations(e.g., a group of users who co-purchase an item) ashyperedges.Hyperedge predictionis to predict future or unobserved hyperedges, which is a fundamental task in many real-world applications (e.g., group recommendation). Despite the recent breakthrough of hyperedge prediction methods, the following challenges have been rarely studied: (C1)How to aggregate the nodes in each hyperedge candidate for accurate hyperedge prediction?and (C2)How to mitigate the inherent data sparsity problem in hyperedge prediction?To tackle both challenges together, in this paper, we propose a novel hyperedge prediction framework ($\mathsf{CASH}$CASH) that employs (1)context-aware node aggregationto precisely capture complex relations among nodes in each hyperedge for (C1) and (2)self-supervised contrastive learningin the context of hyperedge prediction to enhance hypergraph representations for (C2). Furthermore, as for (C2), we propose ahyperedge-aware augmentationmethod to fully exploit the latent semantics behind the original hypergraph and consider both node-level and group-level contrasts (i.e.,dual contrasts) for better node and hyperedge representations. Extensive experiments on six real-world hypergraphs reveal that$\mathsf{CASH}$consistently outperforms all competing methods in terms of the accuracy in hyperedge prediction and each of the proposed strategies is effective in improving the model accuracy of$\mathsf{CASH}$.