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cs.LG2026
Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection
Li Sun, Lanxu Yang, Jiayu Tian +6
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 typica…
cs.LG2024★ 40 cited
Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis
Zhe Liu, Xiang Huang, Jingyun Zhang +3
Unsupervised anomaly detection in time series is essential in industrial applications, as it significantly reduces the need for manual intervention. Multivariate time series pose a…