Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning

TL;DR

This paper introduces CASH, a novel hyperedge prediction framework that combines context-aware node aggregation and self-supervised contrastive learning to improve accuracy and address data sparsity in hypergraphs.

Contribution

CASH innovatively integrates context-aware node aggregation with hyperedge-aware contrastive learning to enhance hyperedge prediction in hypergraphs.

Findings

CASH outperforms existing methods in hyperedge prediction accuracy.

The proposed dual contrast strategy improves node and hyperedge representations.

Extensive experiments validate the effectiveness of each component of CASH.

Abstract

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.