利用上下文感知的自监督学习增强超边预测
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
AI总结：
本文提出超边预测框架CASH，通过上下文感知节点聚合解决节点关系捕获问题，并利用超边感知增强与双重对比的自监督学习缓解数据稀疏，在六个真实数据集上显著提升了预测准确率。
摘要：
超图能够将分组关系（例如共同购买某件商品的一组用户）自然地建模为超边。超边预测旨在预测未来或未观测到的超边，这是许多实际应用（如群组推荐）中的一项基本任务。尽管超边预测方法最近取得了突破，但以下挑战鲜少被研究：（C1）如何聚合每个超边候选中的节点以实现准确的超边预测？以及（C2）如何缓解超边预测中固有的数据稀疏问题？为了共同解决这两个挑战，本文提出了一种新颖的超边预测框架（CASH），该框架采用（1）上下文感知的节点聚合，以精确捕获每个超边中节点间的复杂关系来解决（C1）；以及（2）在超边预测背景下采用自监督对比学习，以增强超图表示来解决（C2）。此外，针对（C2），我们提出了一种超边感知的增强方法，以充分利用原始超图背后的潜在语义，并考虑节点级和群组级对比（即双重对比），以获得更好的节点和超边表示。在六个真实世界超图上的大量实验表明，CASH在超边预测的准确率上始终优于所有竞争方法，并且所提出的每种策略都能有效提升CASH的模型准确率。关于CASH的详细信息，我们在以下网址提供了代码和数据集：https://github.com/yy-ko/cash。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
