计算机工程2026，Vol.52Issue(1)：136-143,8.DOI:10.19678/j.issn.1000-3428.0069952
基于超图神经网络的链路预测方法
Link Prediction Method Based on Hypergraph Neural Network
摘要
Abstract
With the rapid development of information technology,link prediction has been widely applied in various fields.Current link prediction methods are based on subgraph extraction.Models based on Line Graph Transformation(LGT)and Graph Convolutional Network(GCN)achieve excellent results in link prediction.However,two problems remain:1)the high time complexity of the LGT and the large size of the line graph hinder its wide-spread application;2)GCN ignores the high-order relationship and local clustering structure between nodes,thereby affecting prediction accuracy.To solve the above issues,this paper proposes a link prediction method based on Hypergraph Convolutional Network(HGCN),called HGLP.This method replaces LGT with Dual Hypergraph Transformation(DHT)to improve system efficiency without losing structural information and applies HGCN to learn the higher-order features of the hypernodes and hyperedges in the hypergraph to obtain higher prediction accuracy.Experimental results show that the proposed method outperforms state-of-the-art link prediction methods on seven real-world datasets from different domains,in terms of Area Under the Curve(AUC)and Average Precision(AP).Furthermore,the proposed method achieves shorter runtimes and less memory usage.关键词
链路预测/超图/超图神经网络/对偶超图转换/深度学习Key words
link prediction/hypergraph/Hypergraph Convolutional Network(HGCN)/Dual Hypergraph Transformation(DHT)/deep learning分类
信息技术与安全科学引用本文复制引用
陈亮,赵英,史晟辉,尹琳..基于超图神经网络的链路预测方法[J].计算机工程,2026,52(1):136-143,8.基金项目
中央高水平医院临床业务费专项成果转化项目(2023-NHLHCRF-YXHZ-MS-04) （2023-NHLHCRF-YXHZ-MS-04）
北京化工大学-中日友好医院生物医学转化工程研究中心联合项目(XK2023-18). （XK2023-18）
