{"title":"Enhancing Hyperedge Prediction With Context-Aware Self-Supervised Learning","authors":"Yunyong Ko;Hanghang Tong;Sang-Wook Kim","doi":"10.1109/TKDE.2025.3532263","DOIUrl":null,"url":null,"abstract":"Hypergraphs can naturally model <i>group-wise relations</i> (e.g., a group of users who co-purchase an item) as <i>hyperedges</i>. <i>Hyperedge prediction</i> 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: (<b>C1</b>) <i>How to aggregate the nodes in each hyperedge candidate for accurate hyperedge prediction?</i> and (<b>C2</b>) <i>How to mitigate the inherent data sparsity problem in hyperedge prediction?</i> To tackle both challenges together, in this paper, we propose a novel hyperedge prediction framework (<b><inline-formula><tex-math>$\\mathsf{CASH}$</tex-math><alternatives><mml:math><mml:mi>CASH</mml:mi></mml:math><inline-graphic></alternatives></inline-formula></b>) that employs (1) <i>context-aware node aggregation</i> to precisely capture complex relations among nodes in each hyperedge for (C1) and (2) <i>self-supervised contrastive learning</i> in the context of hyperedge prediction to enhance hypergraph representations for (C2). Furthermore, as for (C2), we propose a <i>hyperedge-aware augmentation</i> method to fully exploit the latent semantics behind the original hypergraph and consider both node-level and group-level contrasts (i.e., <i>dual contrasts</i>) for better node and hyperedge representations. Extensive experiments on six real-world hypergraphs reveal that <inline-formula><tex-math>$\\mathsf{CASH}$</tex-math></inline-formula> 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 <inline-formula><tex-math>$\\mathsf{CASH}$</tex-math></inline-formula>.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1772-1784"},"PeriodicalIF":8.9000,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10848355/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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 $\mathsf{CASH}$.
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.