Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detection

Researchers introduce ANOMIX, a new framework that improves graph neural network anomaly detection by generating hard negative samples through mixup techniques. The method addresses the limitation of existing GNN-based detection systems that struggle with subtle boundary anomalies by creating more robust decision boundaries.