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cs.LG2026
GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning
Chuang Liu, Zelin Yao, Xueqi Ma +4
Graph self-supervised learning typically relies on large-scale unlabeled datasets, heavily inflating computational costs. However, empirical evidence suggests that these datasets c…
cs.LG2026
From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection
Luzhi Wang, Xuanshuo Fu, He Zhang +3
Graph Out-of-Distribution (OOD) detection aims to identify whether a test graph deviates from the distribution of graphs observed during training, which is critical for ensuring th…