Multi-Head Spectral-Adaptive Graph Anomaly Detection
In graph-based anomaly detection—particularly for financial fraud—adversarial anomalies exhibit strong camouflage, and high-frequency discriminative signals are often smoothed out or lost by global graph filters. Method: We propose a spectral-fingerprint-driven dynamic graph learning framework tailored for financial fraud detection. First, we design a lightweight hypernetwork that dynamically generates multi-head Chebyshev filters per node based on its spectral fingerprint, enabling instance-adaptive spectral-domain modeling. Second, we introduce a multi-head decoupling mechanism combining Teacher–Student Contrastive (TSC) learning with Barlow Twins diversity regularization to enhance robust representation of anomaly-sensitive features. Results: Evaluated on four real-world heterogeneous graph datasets, our method consistently outperforms state-of-the-art approaches. Crucially, it preserves critical high-frequency anomaly signals under high heterogeneity, leading to improved detection accuracy and generalization.