Enhancing Hyperedge Prediction With Context-Aware Self-Supervised Learning

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yunyong Ko;Hanghang Tong;Sang-Wook Kim
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引用次数: 0

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 ($\mathsf{CASH}$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 $\mathsf{CASH}$ 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 $\mathsf{CASH}$.
利用情境感知自监督学习增强超edge 预测能力
超图可以很自然地将组智能关系(例如,共同购买一件商品的一组用户)建模为超边缘。超边缘预测是预测未来或未观察到的超边缘,这是许多实际应用中的基本任务(例如,组推荐)。尽管近年来超边缘预测方法取得了突破,但对以下挑战的研究很少:(C1)如何聚合每个候选超边缘中的节点以进行准确的超边缘预测?(C2)如何缓解超边缘预测中固有的数据稀疏性问题?为了同时解决这两个挑战,在本文中,我们提出了一个新的超边缘预测框架($\mathsf{CASH}$CASH),该框架采用(1)上下文感知节点聚合来精确捕获(C1)每个超边缘节点之间的复杂关系,(2)超边缘预测上下文中的自监督对比学习来增强(C2)的超图表示。此外,对于(C2),我们提出了一种超边缘感知增强方法,以充分利用原始超图背后的潜在语义,并考虑节点级和组级对比(即双重对比),以获得更好的节点和超边缘表示。在六个真实世界的超图上进行的大量实验表明,$\mathsf{CASH}$在超边缘预测的准确性方面始终优于所有竞争方法,并且所提出的每种策略都有效地提高了$\mathsf{CASH}$的模型准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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