Anomaly Detection Method Based on Graph Convolutional Networks and Community Detection
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.