Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915825518116864 |
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| author | Sun, Li Yang, Lanxu Tian, Jiayu Fang, Bowen Yu, Xiaoyan Ye, Junda Tang, Peng Peng, Hao Yu, Philip S. |
| author_facet | Sun, Li Yang, Lanxu Tian, Jiayu Fang, Bowen Yu, Xiaoyan Ye, Junda Tang, Peng Peng, Hao Yu, Philip S. |
| contents | Detecting out-of-distribution (OOD) graphs is crucial for ensuring the safety and reliability of Graph Neural Networks. In unsupervised graph-level OOD detection, models are typically trained using only in-distribution (ID) data, resulting in incomplete feature space characterization and weak decision boundaries. Although synthesizing outliers offers a promising solution, existing approaches rely on fixed, non-adaptive sampling heuristics (e.g., distance- or density-based), limiting their ability to explore informative OOD regions. We propose a Policy-Guided Outlier Synthesis (PGOS) framework that replaces static heuristics with a learned exploration strategy. Specifically, PGOS trains a reinforcement learning agent to navigate low-density regions in a structured latent space and sample representations that most effectively refine the OOD decision boundary. These representations are then decoded into high-quality pseudo-OOD graphs to improve detector robustness. Extensive experiments demonstrate that PGOS achieves state-of-the-art performance on multiple graph OOD and anomaly detection benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00602 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection Sun, Li Yang, Lanxu Tian, Jiayu Fang, Bowen Yu, Xiaoyan Ye, Junda Tang, Peng Peng, Hao Yu, Philip S. Machine Learning Artificial Intelligence Detecting out-of-distribution (OOD) graphs is crucial for ensuring the safety and reliability of Graph Neural Networks. In unsupervised graph-level OOD detection, models are typically trained using only in-distribution (ID) data, resulting in incomplete feature space characterization and weak decision boundaries. Although synthesizing outliers offers a promising solution, existing approaches rely on fixed, non-adaptive sampling heuristics (e.g., distance- or density-based), limiting their ability to explore informative OOD regions. We propose a Policy-Guided Outlier Synthesis (PGOS) framework that replaces static heuristics with a learned exploration strategy. Specifically, PGOS trains a reinforcement learning agent to navigate low-density regions in a structured latent space and sample representations that most effectively refine the OOD decision boundary. These representations are then decoded into high-quality pseudo-OOD graphs to improve detector robustness. Extensive experiments demonstrate that PGOS achieves state-of-the-art performance on multiple graph OOD and anomaly detection benchmarks. |
| title | Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.00602 |