计算机工程与应用2024，Vol.60Issue(20)：49-67,19.DOI:10.3778/j.issn.1002-8331.2403-0308
动态图神经网络链接预测综述
Survey of Dynamic Graph Neural Network for Link Prediction
摘要
Abstract
Complex dynamic network data,such as social networks,protein interaction networks,and infectious disease transmission networks,are prevalent in the real world,consisting of numerous nodes and edges.Effective mining and utili-zation of such data for accurate prediction have become a key task.Dynamic graph neural network link prediction is an important branch of deep learning research,which aims to analyze the intrinsic laws of network evolution over time and predict potential future linkages,providing valuable information and basis for decision-making in various fields.This paper first reviews the development of dynamic graph neural networks,then introduces the modeling methods and training processes of dynamic graphs.Based on this,the paper categorizes dynamic graph neural network link prediction models into two main types according to the granularity of time:discrete dynamic graph models and continuous dynamic graph models,and provides an overview of the modeling methods used by current mainstream models in each category.In addi-tion,it also introduces commonly used data sets,evaluation indicators and some application scenarios in dynamic graph link prediction research.Finally,the future development trends in this field are discussed prospectively.关键词
图神经网络/深度学习/动态图学习/链接预测/时间图Key words
graph neural network/deep learning/dynamic graph learning/link prediction/temporal graph分类
信息技术与安全科学引用本文复制引用
张其,陈旭,王叔洋,景永俊,宋吉飞..动态图神经网络链接预测综述[J].计算机工程与应用,2024,60(20):49-67,19.基金项目
北方民族大学中央高校基本科研业务费专项资金(2022PT_S04) （2022PT_S04）
宁夏回族自治区重点研发项目(2023BDE02017). （2023BDE02017）
