CHEN X, ZHANG Q, WANG S Y,et al.Geometric interaction-based discrete dynamic graph link prediction model[J].Journal of Henan Polytechnic University(Natural Science) ,2025,44(5):52-61.

DOI:10.16186/j.cnki.1673-9787.2024070021

Received: 2024/07/04

Revised: 2024/09/30

Published:2025/07/23

Geometric interaction-based discrete dynamic graph link prediction model

Chen Xu1, Zhang Qi1, Wang Shuyang2, Jing Yongjun1

1.College of Computer Science and Engineering， North Minzu University， Yinchuan  750000， Ningxia， China；2.College of Electrical and Information Engineering， North Minzu University， Yinchuan  750000， Ningxia， China

Abstract:With the widespread application of complex network analysis in many fields， such as recommendation 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 problem 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 hyperbolic 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 prediction probabilities in Euclidean and hyperbolic spaces were calculated through the probability interaction fusion 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 prediction 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 existing 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.

Key words:discrete dynamic graph;representation learning;link prediction;hyperbolic space;geometric deep learning