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Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
arXiv – CS AI|Li Sun, Lanxu Yang, Jiayu Tian, Bowen Fang, Xiaoyan Yu, Junda Ye, Peng Tang, Hao Peng, Philip S. Yu||6 views
🤖AI Summary
Researchers propose PGOS (Policy-Guided Outlier Synthesis), a new framework that uses reinforcement learning to improve Graph Neural Network safety by better detecting out-of-distribution graphs. The system replaces static sampling methods with a learned exploration strategy that navigates low-density regions to generate pseudo-OOD graphs for enhanced detector training.
Key Takeaways
- →PGOS framework uses reinforcement learning agents to improve out-of-distribution detection in Graph Neural Networks.
- →The system replaces fixed sampling heuristics with adaptive exploration strategies for better OOD region discovery.
- →The framework generates high-quality pseudo-OOD graphs to strengthen decision boundaries and improve detector robustness.
- →Extensive experiments show PGOS achieves state-of-the-art performance on multiple graph OOD and anomaly detection benchmarks.
- →The research addresses critical safety and reliability concerns for Graph Neural Network deployment in real-world applications.
#graph-neural-networks#out-of-distribution-detection#reinforcement-learning#machine-learning-safety#anomaly-detection#ai-research#neural-network-robustness
Read Original →via arXiv – CS AI
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