Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection
The SignGAD framework reformulates graph anomaly detection by replacing fixed pipelines with self-designed task-conditioned workflows, and introduces a guarded final refit strategy to improve reliability under limited supervision.
Article intelligence
Key points
- SignGAD shifts from training a fixed detector to designing detection workflows
- It selects suitable graph encodings and detector designs for task-specific anomaly evidence
- A guarded final refit strategy calibrates refit acceptance to enhance reliability
- Experiments on real-world datasets show strong performance against state-of-the-art methods
Why it matters
This matters because signGAD shifts from training a fixed detector to designing detection workflows.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.27470] Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection
[Submitted on 26 May 2026]
Title:Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection
View a PDF of the paper titled Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection, by Tairan Huang and 6 other authors
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Abstract:Graph anomaly detection aims to identify anomaly nodes in attributed graphs and plays an important role in real-world applications. However, existing graph anomaly detection methods still face two key challenges: 1) fixed pipelines, which restrict their adaptability across different graph tasks under limited supervision; 2) weak evidence, which prevents them from explicitly incorporating contextual and structural anomaly signals into the detection process. In this paper, we propose a novel framework, self-designing agentic workflows for few-shot graph anomaly detection (SignGAD). Specifically, we propose a novel paradigm that reformulates graph anomaly detection task from training a fixed anomaly detector to designing task-conditioned detection workflows. By constructing detection workflows, SignGAD selects suitable graph encodings and detector designs to exploit task-specific anomaly evidence. Meanwhile, we introduce a guarded final refit strategy to refine the selected workflow by calibrating refit acceptance, enhancing reliability under limited supervision. Extensive experiments conducted on several real-world datasets demonstrate that SignGAD achieves strong performance against state-of-the-art methods, highlighting its effectiveness on graph anomaly detection tasks.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.27470 [cs.LG]
(or arXiv:2605.27470v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.27470
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tairan Huang [view email] [v1] Tue, 26 May 2026 09:16:34 UTC (32,191 KB)
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