  Hypergraphs can naturally model group-wise relations (e.g., a group of users
who co-purchase an item) as hyperedges. Hyperedge prediction is 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 (CASH) that
employs (1) context-aware node aggregation to precisely capture complex
relations among nodes in each hyperedge for (C1) and (2) self-supervised
contrastive learning in the context of hyperedge prediction to enhance
hypergraph representations for (C2). Furthermore, as for (C2), we propose a
hyperedge-aware augmentation method 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 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 CASH. For the detailed information of CASH, we
provide the code and datasets at: https://github.com/yy-ko/cash.
