From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection

• URL: http://arxiv.org/abs/2602.17342v1
• Date: Thu, 19 Feb 2026 13:19:53 GMT
• Title: From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection
• Authors: Luzhi Wang, Xuanshuo Fu, He Zhang, Chuang Liu, Xiaobao Wang, Hongbo Liu,
• Abstract summary: Graph Out-of-Distribution (OOD) detection aims to identify whether a test graph deviates from the distribution observed during training.<n>We propose a textbfSelf-textbfIm textbfGraph textbfOut-textbfof-textbfDistribution detector (SIGOOD)<n>SIGOOD generates a prompt to construct a prompt-enhanced graph that amplifies potential OOD signals.
• Score: 15.167705236111104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Out-of-Distribution (OOD) detection aims to identify whether a test graph deviates from the distribution of graphs observed during training, which is critical for ensuring the reliability of Graph Neural Networks (GNNs) when deployed in open-world scenarios. Recent advances in graph OOD detection have focused on test-time training techniques that facilitate OOD detection without accessing potential supervisory information (e.g., training data). However, most of these methods employ a one-pass inference paradigm, which prevents them from progressively correcting erroneous predictions to amplify OOD signals. To this end, we propose a \textbf{S}elf-\textbf{I}mproving \textbf{G}raph \textbf{O}ut-\textbf{o}f-\textbf{D}istribution detector (SIGOOD), which is an unsupervised framework that integrates continuous self-learning with test-time training for effective graph OOD detection. Specifically, SIGOOD generates a prompt to construct a prompt-enhanced graph that amplifies potential OOD signals. To optimize prompts, SIGOOD introduces an Energy Preference Optimization (EPO) loss, which leverages energy variations between the original test graph and the prompt-enhanced graph. By iteratively optimizing the prompt by involving it into the detection model in a self-improving loop, the resulting optimal prompt-enhanced graph is ultimately used for OOD detection. Comprehensive evaluations on 21 real-world datasets confirm the effectiveness and outperformance of our SIGOOD method. The code is at https://github.com/Ee1s/SIGOOD.

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