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Graph Neural Networks (GNNs) are widely applied for graph anomaly detection.As one of the key components for GNN design is to select a tailored spectralfilter, we take the first step towards analyzing anomalies via the lens of thegraph spectrum. Our crucial observation is the existence of anomalies will leadto the `right-shift' phenomenon, that is, the spectral energy distributionconcentrates less on low frequencies and more on high frequencies. This factmotivates us to propose the Beta Wavelet Graph Neural Network (BWGNN). Indeed,BWGNN has spectral and spatial localized band-pass filters to better handle the`right-shift' phenomenon in anomalies. We demonstrate the effectiveness ofBWGNN on four large-scale anomaly detection datasets. Our code and data arereleased at https://github.com/squareRoot3/Rethinking-Anomaly-Detection

Rethinking Graph Neural Networks for Anomaly Detection