计算机工程与应用2024，Vol.60Issue(13)：51-65,15.DOI:10.3778/j.issn.1002-8331.2310-0234
图神经网络在异常检测中的应用综述
Survey of Application of Graph Neural Network in Anomaly Detection
摘要
Abstract
Graph data is commonly used to represent complex relationships between different individuals,such as social networks,financial networks,and microservice networks.Graph neural network(GNN)is a deep learning model used for processing graph data,which can effectively capture structural and feature information in graph data.Anomaly detection refers to identifying unexpected data from a massive amount of data.Traditional anomaly detection methods usually do not consider the relationships between data when detecting graph data,while models that use GNN for anomaly detection can learn from graph structures and features,thereby improving the accuracy and robustness of anomaly detection.This paper reviews the application of GNN in anomaly detection from three aspects.Firstly,the basic framework of GNN is introduced.Secondly,the latest research progress of GNN in static graph anomaly detection,dynamic graph anomaly detection,and time series data anomaly detection is discussed separately.Finally,an in-depth analysis is conducted on the future research directions in this field.关键词
图神经网络/异常检测/静态图/动态图/时序数据Key words
graph neural network/anomaly detection/static graph/dynamic graph/time series data分类
信息技术与安全科学引用本文复制引用
陈佳乐,陈旭,景永俊,王叔洋..图神经网络在异常检测中的应用综述[J].计算机工程与应用,2024,60(13):51-65,15.基金项目
北方民族大学中央高校基本科研业务费专项资金(2023ZRLG13) （2023ZRLG13）
宁夏回族自治区重点研发项目(2023BDE02017) （2023BDE02017）
北方民族大学研究生创新项目(YCX23161). （YCX23161）
