What Remains Without Test Labels? Re-evaluating a Graph-Level Anomaly Detection Benchmark
Graph-level anomaly detectors are trained only on normal graphs, yet benchmarks may use test labels for model selection. We examine whether UB-GOLD results reused by later papers support label-free detection. We audit UB-GOLD and 14 GLAD code bases, re-run 21 detectors on UB-GOLD's folds under selection rules with and without test labels, and evaluate sensitivity to known structural changes. Test labels select checkpoints in UB-GOLD's shared training loop and checkpoints or hyperparameters in 8 of the 14 code bases. UB-GOLD also evaluates its kernel baselines against misaligned labels. Across nine datasets, selecting checkpoints by test AUROC adds an average of 5.12 AUROC points [3.97, 6....
| Authors | Yujie Lu, Da Yan |
|---|---|
| Primary category | cs.LG |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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