On the Effectiveness of Resampling for Supervised Graph Anomaly Detection

When does resampling help supervised graph anomaly detection? This empirical study compares resampling across 17 benchmarks and two tree-ensemble learners, holding graph-derived features and labelled splits fixed. We use allocation, transformation, and the weight rule to distinguish where resampling gains arise. The clearest gains occur on injected anomalies: simple feature perturbation improves test AUPRC over class weighting by 5.22 points with XGBoost and 4.40 with Random Forest; its smaller organic gains have confidence intervals including zero. Under scarce labels, labelled-split variation exceeds strategy variation in the organic panel: a descriptive decomposition assigns 72% versus 10% to these factors for XGBoost, and 68% versus 16% for Random Forest. Adaptive controls establish a small gain from residual-guided allocation and weighting, while the additional benefit of structural transformation remains unresolved. These findings motivate separating anomaly provenance, repeating paired labelled splits, and testing transformations against unchanged copies with matched allocation and class mass.