计算机科学与探索2026，Vol.20Issue(5)：1207-1240,34.DOI:10.3778/j.issn.1673-9418.2505028
图神经网络驱动的图异常检测研究综述
Survey of Graph Anomaly Detection Driven by Graph Neural Networks
摘要
Abstract
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.关键词
图异常检测/图表示学习/图神经网络/静态图异常/动态图异常Key words
graph anomaly detection/graph representation learning/graph neural networks/static graph anomaly/dynamic graph anomaly分类
信息技术与安全科学引用本文复制引用
徐登彬,袁立宁,吴沛宸,刘钊..图神经网络驱动的图异常检测研究综述[J].计算机科学与探索,2026,20(5):1207-1240,34.基金项目
国家重点研发计划(2023YFC3321604) （2023YFC3321604）
广西哲学社会科学研究课题(23FTQ005).This work was supported by the National Key Research and Development Program of China(2023YFC3321604),and the Research Projects in Philosophy and Social Sciences of Guangxi Zhuang Autonomous Region(23FTQ005). （23FTQ005）
