SOURCE-LINKED INTELLIGENCE

Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global