RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection

arXiv:2609.02497 · cs.LG, cs.AI · Submitted 2026-09-02 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-02

Updated: 2026-09-02

Terminology

• Calibration — 11× in this paper · explained in 7 episodes
• Adaptation — 8× in this paper · explained in 1 episode
• Embeddings — 8× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• AUC — 3× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Robust Iterative Normality Self-Estimation — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

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