Learning with Blacklists on Graphs via Prototype-Guided Dual-Frequency Filtering
A scalable fraud detection framework that integrates prototype-guided graph abstraction with dualfrequency filtering to effectively learn from limited blacklists is proposed, and demonstrates that the proposed method consistently outperforms state-of-the-art approaches.