Related papers: Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection

Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection

URL: http://arxiv.org/abs/2603.00602v1
Date: Sat, 28 Feb 2026 11:40:18 GMT
Title: Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
Authors: Li Sun, Lanxu Yang, Jiayu Tian, Bowen Fang, Xiaoyan Yu, Junda Ye, Peng Tang, Hao Peng, Philip S. Yu,
Abstract summary: In unsupervised graph-level OOD detection, models are typically trained using only in-distribution (ID) data.<n>We propose a Policy-Guided Outlier Synthesis framework that replaces statics with a learned exploration strategy.
Score: 51.93878677594561
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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