学习探索：基于策略的异常合成用于图分布外检测
Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
AI总结：
PGOS通过强化学习策略生成高质量伪OOD图，提升图分布外检测的鲁棒性与性能
摘要：
检测分布外（OOD）图对于确保图神经网络的安全性和可靠性至关重要。在无监督的图级OOD检测中，模型通常仅使用分布内（ID）数据进行训练，导致特征空间描述不完整和决策边界薄弱。尽管合成异常值提供了一种有前景的解决方案，但现有方法依赖于固定、非适应性的采样启发式（例如基于距离或密度的），限制了它们探索信息性OOD区域的能力。我们提出了一个基于策略的异常合成（PGOS）框架，用学习的探索策略替代静态启发式。具体而言，PGOS训练一个强化学习代理在结构化的潜在空间中导航低密度区域，并采样最能优化OOD决策边界的表示。这些表示随后被解码为高质量的伪OOD图以提高检测器的鲁棒性。广泛的实验表明，PGOS在多个图OOD和异常检测基准上实现了最先进的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
