河南理工大学学报（自然科学版）2025，Vol.44Issue(5)：52-61,10.DOI:10.16186/j.cnki.1673-9787.2024070021
基于几何交互的离散动态图链接预测模型
Geometric interaction-based discrete dynamic graph link prediction model
摘要
Abstract
With the widespread application of complex network analysis in many fields,such as recommen-dation systems,social networks,disease transmission networks,and financial transaction networks,the analysis of dynamic graphs has become a key challenge in the study of graph neural networks.Objectives The single geometric space embedding method in the dynamic graph link prediction task often has the prob-lem of embedding distortion,which makes it difficult to effectively capture the hierarchical and regular structures in complex networks.Methods A geometric interaction-based discrete dynamic graph(GIDG)link prediction model was proposed.Firstly,feature aggregation was performed in Euclidean space and hy-perbolic space respectively to extract the embedding features of regular structure and hierarchical structure.Secondly,the two geometric features were interactively fused to obtain more expressive node embedding.Then,a historical information fusion module was designed to balance the fusion of long-term information and short-term information,further improving the prediction ability of time series.Finally,the link predic-tion probabilities in Euclidean and hyperbolic spaces were calculated through the probability interaction fu-sion module,and the final link prediction results were obtained through adaptive weighted fusion.Results Experimental results showed that GIDG outperformed the advanced baseline models based on Euclidean space and hyperbolic space on five datasets.The average gains of AUC indicators in dynamic link predic-tion and dynamic new link prediction tasks were 1.46%and 0.81%,and the average gains of AP indicators were 1.27%and 1.70%,respectively.Especially on large datasets,GIDG significantly outperformed the ex-isting advanced baseline models,especially when dealing with complex hierarchical structures and power-law distribution graphs.Conclusions GIDG effectively solved the embedding distortion problem of single space embedding methods,could better capture the hierarchical structure and regular structure of complex networks,and significantly improves the dynamic link prediction effect.关键词
离散动态图/表示学习/链接预测/双曲空间/几何深度学习Key words
discrete dynamic graph/representation learning/link prediction/hyperbolic space/geometric deep learning分类
信息技术与安全科学引用本文复制引用
陈旭,张其,王叔洋,景永俊..基于几何交互的离散动态图链接预测模型[J].河南理工大学学报（自然科学版）,2025,44(5):52-61,10.基金项目
中央高校基本科研业务费专项资金资助项目(2023ZRLG13) （2023ZRLG13）
宁夏回族自治区重点研发项目(2023BDE02017) （2023BDE02017）
