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This paper introduces JPGFN, a novel graph anomaly detection method that addresses key limitations in existing frequency-domain filtering approaches. By employing a Feature Separation Transformation Network (FSTNN) for enhanced fine-grained feature learning and an adaptive Jacobi polynomial graph filtering module, JPGFN effectively captures complex frequency-domain characteristics of graph signals. Experimental results show that JPGFN significantly outperforms mainstream methods across various real-world datasets, highlighting its potential for improved anomaly detection in graph data.
JPGFN outperforms traditional methods by leveraging adaptive filtering and fine-grained feature learning to enhance graph anomaly detection.
In recent years, graph anomaly detection (GAD) based on frequency-domain filtering have achieved promising results. However, existing approaches still face three major challenges: First, they use static basic function to constructed graph filter which cannot effectively adapt to the frequency-domain distribution of graph data. Second, they fail to adequately consider the importance information of each attribute in the node feature vector, leading to the loss of fine-grained information. Third, they insufficiently utilize node labels for GAD. To address these issues, this paper proposes a novel graph anomaly detection method called JPGFN (Feature Transformation Enhanced Jacobi Polynomial Graph Filtering Network). First, a Feature Separation Transformation Network (FSTNN) is developed to better learn fine-grained node features by feature separation and applying nonlinear transformations to node features across different dimensions. Second, an adaptive Jacobi polynomial graph filtering module is constructed based on Jacobi polynomials to adaptively capture complex frequency-domain features of graph signals. Finally, a node label constraint module is developed to facilitate the use of node labels and enhance the performance of GAD. Experimental results on multiple real-world datasets demonstrate that the proposed method significantly outperforms mainstream approaches.