计算机工程与应用2024，Vol.60Issue(14)：37-49,13.DOI:10.3778/j.issn.1002-8331.2308-0216
Graph Transformers研究进展综述
Overview of Research Progress in Graph Transformers
摘要
Abstract
With the widespread application of graph structured data in various practical scenarios,the demand for effective modeling and processing is increasing.Graph Transformers(GTs),as a type of model that uses Transformers to process graph data,can effectively alleviate the problems of over smoothing and over squeezing in traditional graph neural net-work(GNN),and thus can learn better feature representations.Firstly,based on the research on recent GTs related litera-ture,the existing model architectures are divided into two categories:the first category adds graph position and structure information to Transformers through absolute encoding and relative encoding to enhance Transformers'understanding and processing ability of graph structure data;the second type combines GNN with Transformers in different ways(serial,alternating,parallel)to fully utilize their advantages.Secondly,the application of GTs in fields such as information security,drug discovery,and knowledge graphs is introduced,and the advantages and disadvantages of models with different uses are compared and summarized.Finally,the challenges faced by future research on GTs are analyzed from aspects such as scalability,complex graphs,and better integration methods.关键词
Graph Transformers(GTs)/图神经网络/图表示学习/异构图Key words
Graph Transformers(GTs)/graph neural network/graph representation learning/heterogeneous graph分类
信息技术与安全科学引用本文复制引用
周诚辰,于千城,张丽丝,胡智勇,赵明智..Graph Transformers研究进展综述[J].计算机工程与应用,2024,60(14):37-49,13.基金项目
宁夏重点研发计划(引才专项)项目(2022YCZX0013) （引才专项）
宁夏重点研发计划(重点)项目(2023BDE02001) （重点）
北方民族大学2022年校级科研平台《数字化农业赋能宁夏乡村振兴创新团队》(2022PT_S10) （2022PT_S10）
银川市校企联合创新项目(2022XQZD009). （2022XQZD009）
