研究人员提出JPGFN方法，通过特征分离变换和自适应图滤波提升图异常检测性能，比现有方法更优。
JPGFN方法结合特征分离变换网络和雅可比多项式图滤波模块，在多个真实数据集上显著优于主流方法。该方法解决了现有图异常检测技术面临的三个主要挑战：静态基础函数无法适应图数据频率分布、节点特征向量属性重要性考虑不足、节点标签利用不充分。实验结果表明，JPGFN在图异常检测任务中取得了突破性进展。
Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for 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.