Angle-based Graph Neural Network Method for Anomaly Detection in High Dimensional Data
In high-dimensional data spaces,most data are located at the edges of the high-dimensional space and distributed sparsely,resulting in the problem of"curse of dimensionality",which makes existing anomaly detection methods unable to ensure the accuracy of anomaly detection.To address this problem,an Angle-based Graph Neural Network(A-GNN)high-dimensional data anomaly detection method is proposed.First,the data used for training are expanded by uniformly sampling the data space and perturbing the initial training data.Second,the k-nearest neighbor relationship is used to construct a k-nearest neighbor relationship graph of the training data,and the variance of the k-nearest neighbor element distance weighted angle is used as the initial anomaly factor for the nodes in the k-nearest neighbor relationship graph.Finally,by training a GNN model,information exchange between nodes is achieved,enabling adjacent nodes to learn from each other and effectively evaluate anomalies.The A-GNN method is experimentally compared with nine typical anomaly detection methods on six natural datasets.The results demonstrate that A-GNN achieved the highest Area Under the Curve(AUC)value in five datasets,which can significantly improve the anomaly detection accuracy of various dimensions of data.On some true high-dimensional data,the AUC of anomaly detection increased by more than 40%.Compared with three k-nearest neighbor-based anomaly detection methods at different k values,A-GNN can effectively avoid the impact of k values on detection results by utilizing information exchange between GNN nodes,and the method has stronger robustness.
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