BNFlow: Adaptive Flow Guidance for Graph-Level Anomaly Detection

Graph-level anomaly detection (GLAD) identifies graphs that deviate from a normal population. With only normal graphs available for training, existing methods typically characterize normal patterns or generate pseudo anomalies for negative supervision. However, generated negatives can be too dissimilar to normal graphs or become too easy as the detector improves, limiting their usefulness for detecting subtle anomalies. To address this, we propose BNFlow, a guided flow matching framework that combines a normal graph reference with adaptive sample refinement. Flow matching learns transport toward normal graph representations, and detector feedback refines intermediate samples to make them more challenging as negatives. Refinement is bounded within each trajectory, and its strength adapts to the detector's ability to distinguish normal and generated graphs. Continued flow matching regularizes the generator toward the normal reference during joint learning. Our theoretical analysis characterizes the local effect of guidance and bounds deviation from normal references. Experiments on eight graph benchmarks demonstrate superior overall performance over state-of-the-art GLAD methods. Further analyses highlight the complementary roles of normal transport and detector guidance in providing effective negative supervision.