Generalist Graph Anomaly Detection via Prototype-Based Distillation

arXiv:2605.26857 · cs.LG · Submitted 2026-05-26 · Read on arXiv

cs.LG

Submitted: 2026-05-26

Updated: 2026-08-29

Code: https://github.com/yimingxu24/ProMoS

Terminology

• Distillation — 65× in this paper · explained in 2 episodes
• AUROC — 40× in this paper · explained in 2 episodes
• Generalization — 15× in this paper · explained in 13 episodes
• Robustness — 13× in this paper · explained in 5 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Embeddings — 7× in this paper · explained in 2 episodes
• Knowledge Distillation — 7× in this paper · explained in 11 episodes
• Contrastive Learning — 6× in this paper · explained in 5 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Self-Supervised Learning — 5× in this paper · explained in 8 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 4× in this paper
• Clustering — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• GNNs — 3× in this paper · explained in 2 episodes
• Generalizability — 3× in this paper · explained in 6 episodes
• Transferability — 3× in this paper · explained in 4 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Foundation Model — 2× in this paper · explained in 3 episodes
• Graph Neural Network (GNN) — 2× in this paper · explained in 3 episodes
• Heterogeneous Graph — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• In Prototype-guided Soft-label Distillation — 1× in this paper
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• Universal Link Predictor By In-Context Learning on Graphs
• GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment
• A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
• Pitfalls of Graph Neural Network Evaluation
• PyG-SSL: A Graph Self-Supervised Learning Toolkit

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