图神经网络驱动的图异常检测研究综述OA

Graph anomaly detection has been widely applied in network security,financial transaction monitoring,and social network analysis,aiming to identify anomalous nodes,edges,or subgraphs within graph databases.Leveraging their ability to model complex structural patterns,graph neural networks have emerged as a key approach in this domain.However,existing surveys exhibit gaps in coverage and classification coarseness.To address these shortcomings,this survey systematically re-views graph neural network-based anomaly detection research from three perspectives.It provides precise definitions of different anomaly types in graph data,and introduces a refined classification framework by grouping models according to graph data type(static vs.dynamic)and anomaly level(node,edge,subgraph,graph-level,and multi-level).It traces the development and representative architectures of various methods through the lenses of joint embedding and anomaly detection optimization,contrastive learning,and autoencoder-based reconstruction.It compiles mainstream public datasets,evaluates the performance of representative models,and delves into major challenges such as robustness,efficiency,cross-domain transferability,and lightweight deployment.Based on this analysis,the survey outlines future research directions for multi-level anomaly detection,cross-domain adaptation,and lightweight scenarios.