用强化学习生成更有效的异常图，提升图神经网络的异常检测能力
Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
- 训练智能体在潜在空间探索低密度区域，自适应生成异常样本
- 在多个基准上达到最优性能，显著提升异常检测准确率
- 适合需要高可靠性的图数据安全场景，如金融风控、医疗诊断
图神经网络的安全与可靠性依赖于对分布外（OOD）图的检测。在无监督图级OOD检测中，模型仅使用分布内（ID）数据训练，导致特征空间表征不完整，决策边界薄弱。尽管合成异常样本是可行方案，但现有方法依赖固定、非自适应的采样启发式（如距离或密度相关），难以有效探索有信息量的OOD区域。本文提出策略引导的异常样本合成框架（PGOS），以可学习的探索策略替代静态启发式。具体地，PGOS训练一个强化学习智能体，在结构化潜在空间中导航低密度区域，采样能最有效优化OOD决策边界的表示。这些表示被解码为高质量伪异常图，从而增强检测器鲁棒性。大量实验表明，PGOS在多个图级OOD与异常检测基准上达到当前最佳性能。
原文摘要 · Abstract (English)
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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