Graph Anomaly Detection Based on Local Differential and Global Spectral Fusion
XU Dengbin1, YUAN Lining2,3, WU Peichen1, LIU Zhao4
1. School of Information Network Security, People's Public Security University of China, Beijing 100038; 2. School of National Security, People's Public Security University of China, Beijing 100038; 3. School of Information Technology, Guangxi Police College, Nanning 530028; 4. Cyber Security and Artificial Intelligence Research Center, People's Public Security University of China, Beijing 100038
摘要 图神经网络在图异常检测任务中性能较优,但现有研究大多存在难以识别异常伪装、标签稀缺条件下性能不足等问题.针对上述问题,文中提出基于局部差分与全局谱融合的图异常检测方法(Graph Anomaly Detection Based on Local Differential and Global Spectral Fusion, GAD-LDSF),同时从空间域与频域进行特征嵌入,提升在异常伪装与标签稀缺条件下的检测性能.首先,利用基于软量化的概率自适应嵌入实现数据增强,通过图差分注意力网络精准捕获节点间的细微差异,生成空间域节点表示.然后,构建全局同构化动态频谱增强,利用Chebyshev多项式高效捕捉全局范围的异常信号,生成频域节点表示.最后,动态融合空间域与频域节点表示,并利用预测器实现异常节点检测.在3个基准数据集上的实验表明,GAD-LDSF整体性能较优,尤其是在应对异常伪装与标签稀缺等挑战时表现出较强的鲁棒性与泛化性.
Abstract：Graph neural networks(GNNs) are widely recognized for their effectiveness in graph anomaly detection tasks. However, existing methods commonly struggle to identify camouflaged anomalies and exhibit poor performance under label scarcity. To address these issues, a method for graph anomaly detection based on local differential and global spectral fusion(GAD-LDSF) is proposed. Feature embeddings are employed in both spatial and spectral domains to enhance performance under camouflaged anomalies and label scarcity. First, probability-adaptive continuum embedding based on soft quantization is utilized to achieve data augmentation. A graph differential attention network is employed to accurately capture subtle differences between nodes and generate node representations in the spatial domain. Then, a globally homogenized dynamic spectral enhancement module is constructed. Chebyshev polynomials are leveraged to efficiently capture global anomaly signals and generate node representations in the spectral domain. Finally, spatial domain and spectral domain node representations are dynamically fused, and a predictor is employed to achieve anomalous node detection. Experiments on three benchmark datasets validate that GAD-LDSF achieves overall superior performance, particularly demonstrating its strong robustness and generalization in handling challenges such as camouflaged anomalies and label scarcity.
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2025, Vol. 38 
