The Selection Rule Decides the Winner: A Pre-Registered Audit of Open-Set Graph Anomaly Detection

arXiv:2609.33370 · cs.LG · Submitted 2026-09-27 · Read on arXiv

cs.LG

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• AUC — 127× in this paper · explained in 2 episodes
• Pseudo-labeling — 34× in this paper · explained in 3 episodes
• Benchmark — 20× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Provenance — 8× in this paper · explained in 3 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Open Graph Benchmark — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Semi-supervised Learning — 2× in this paper · explained in 4 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Conformal Prediction — 1× in this paper · explained in 5 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Exchangeability — 1× in this paper · explained in 2 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• Fast Graph Representation Learning with PyTorch Geometric
• Adam: A Method for Stochastic Optimization
• Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
• Pitfalls of Graph Neural Network Evaluation

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