Graph Transformer技术与研究进展:从基础理论到前沿应用  

Graph Transformer technology and research progress:fromfundamental theory to cutting-edge applications

作　　者：游浩 丁苍峰 马乐荣[1] 延照耀 曹璐 You Hao;Ding Cangfeng;Ma Lerong;Yan Zhaoyao;Cao Lu(College of Mathematics&Computer Science,Yan’an University,Yan’an Shaanxi 716000,China)

机构地区：[1]延安大学数学与计算机科学学院,陕西延安716000

出　　处：《计算机应用研究》2025年第4期975-986,共12页Application Research of Computers

基　　金：国家自然科学基金资助项目(62262067);陕西省人才资助项目(YAU202213065,CXY202107);延安大学十四五重大科研资助项目(2021ZCQ012);延安大学基础项目(YDBK2018-35,D2022034);延安大学研究生教育创新计划资助项目(YCX2024049);教学改革研究项目(YDJG23-27)。

摘　　要：图数据处理是一种用于分析和操作图结构数据的方法,广泛应用于各个领域。Graph Transformer作为一种直接学习图结构数据的模型框架,结合了Transformer的自注意力机制和图神经网络的方法,是一种新型模型。通过捕捉节点间的全局依赖关系和精确编码图的拓扑结构,Graph Transformer在节点分类、链接预测和图生成等任务中展现出卓越的性能和准确性。通过引入自注意力机制,Graph Transformer能够有效捕捉节点和边的局部及全局信息,显著提升模型效率和性能。深入探讨Graph Transformer模型,涵盖其发展背景、基本原理和详细结构,并从注意力机制、模块架构和复杂图处理能力(包括超图、动态图)三个角度进行细分分析。全面介绍Graph Transformer的应用现状和未来发展趋势,并探讨其存在的问题和挑战,提出可能的改进方法和思路,以推动该领域的研究和应用进一步发展。Graph data processing is a method used for analyzing and manipulating graph-structured data,which is widely applied across various domains.The Graph Transformer,as a model framework directly learning from graph-structured data,combines the self-attention mechanism of the Transformer and methods from graph neural networks,making it a novel model.By capturing global dependencies between nodes and accurately encoding the topology of graphs,the Graph Transformer exhi-bits outstanding performance and accuracy in tasks such as node classification,link prediction,and graph generation.With the introduction of the self-attention mechanism,the Graph Transformer effectively captures both local and global information of nodes and edges,significantly enhancing model efficiency and performance.This paper delved into the Graph Transformer model,covering its development background,fundamental principles,and detailed structure,and analyzed it from three perspectives:attention mechanisms,modular architecture,and complex graph processing capabilities(including hypergraphs and dynamic graphs).It comprehensively introduced the current application status and future development trends of the Graph Transformer,discussed existing issues and challenges,and proposed possible improvements and ideas to further advance research and applications in this field.

关 键 词：图神经网络 Graph Transformer 图表示学习 节点分类 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]