RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection
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Computer Science > Machine Learning
arXiv:2609.02497 (cs)
[Submitted on 2 Sep 2026]
Title:RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection
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Abstract:Zero-shot graph anomaly detection seeks to deploy a detector trained on source graphs to unseen, unlabeled targets, yet domain shift can make source-derived notions of normality unreliable. We introduce RINSE (Robust Iterative Normality Self-Estimation), a gradient-free target-time framework that keeps the source-trained detector fixed while sequentially estimating target normality, representation calibration, and evidence reliability from the target graph. Its core idea is to identify a reliable subset of low-residual target nodes, use them to construct a trimmed target-aware normality model, and combine complementary anomaly evidence through reliability-gated rank fusion and encoder ensembling. Across eight unseen target graphs, RINSE achieves the highest average AUPRC among the evaluated methods under two separate preprocessing protocols, while block ablations and sensitivity analyses support the combined design. These results support robust target-time estimation as a practical approach to generalist graph anomaly detection without target labels, gradients, or per-target tuning.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.02497 [cs.LG] |
| (or arXiv:2609.02497v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.02497
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Taufikur Rahman Fuad [view email][v1] Wed, 2 Sep 2026 12:03:58 UTC (474 KB)
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View a PDF of the paper titled RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection, by Taufikur Rahman Fuad and 2 other authors
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