论文标题
重新思考图形神经网络以进行异常检测
Rethinking Graph Neural Networks for Anomaly Detection
论文作者
论文摘要
图形神经网络（GNN）被广泛应用于图异常检测。作为GNN设计的关键组件之一是选择量身定制的光谱滤波器，我们迈出了通过图谱镜头分析异常的第一步。我们的关键观察是存在异常的存在将导致“右移”现象，即光谱能量分布较少集中在低频上，而更多地集中在高频上。这个事实促使我们提出β小波图神经网络（BWGNN）。实际上，BWGNN具有光谱和空间局部循环滤波器，以更好地处理异常中的“右移”现象。我们证明了BWGNN对四个大规模异常检测数据集的有效性。我们的代码和数据在https://github.com/squareroot3/rethinking-anomaly-detection上发布
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 spectral filter, we take the first step towards analyzing anomalies via the lens of the graph spectrum. Our crucial observation is the existence of anomalies will lead to the `right-shift' phenomenon, that is, the spectral energy distribution concentrates less on low frequencies and more on high frequencies. This fact motivates 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 of BWGNN on four large-scale anomaly detection datasets. Our code and data are released at https://github.com/squareRoot3/Rethinking-Anomaly-Detection
