Graph Transformers研究进展综述OA北大核心CSTPCD

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.