软件导刊2024，Vol.23Issue(12)：58-65,8.DOI:10.11907/rjdk.241509
基于图卷积网络与社群发现的异常检测方法
Anomaly Detection Method Based on Graph Convolutional Networks and Community Detection
摘要
Abstract
The field of deep learning is paying increasing attention to graph structured data,and multiple fields have abstracted entities into attribute networks.Knowledge graphs and other organizational methods have successfully achieved efficient organization and management of in-formation.In these information rich networks,security issues are particularly important as the presence of anomalous entities may pose a threat to overall interests.Traditional methods face certain difficulties in anomaly detection of graph structured data,especially in capturing high-di-mensional network features.Although deep learning methods are powerful,due to the limitations of network depth,it is often difficult to obtain global information from the network.Therefore,a two-stage anomaly detection method based on graph convolutional neural network is pro-posed,which gradually obtains the community information of nodes through graph convolutional neural network,overcoming the shortcomings of traditional methods in capturing high-dimensional features;Simultaneously considering the node's own attributes to better adapt to various complex network structures and improve anomaly detection performance.The experimental results show that the AUC score of this method ex-ceeds 0.9 on some datasets,and it can achieve optimal or suboptimal performance compared to the baseline method on each dataset.关键词
异常检测/图卷积网络/社群发现/属性网络Key words
anomaly detection/graph convolutional networks/community detection/attributed network分类
信息技术与安全科学引用本文复制引用
夏飞,赵新建,王恺祺,陈石..基于图卷积网络与社群发现的异常检测方法[J].软件导刊,2024,23(12):58-65,8.基金项目
国网江苏省电力有限公司科技项目(J2023178) （J2023178）
