Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation

By PulseAugur Editorial · [1 sources] · 2026-06-02 04:00

Researchers have developed a new framework called SafeSubspace Pseudo-Label Refinement (S$^2$PLR) to improve the accuracy of AI models in source-free graph domain adaptation. This method addresses the challenge of unreliable pseudo-labels by identifying a AI

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arXiv cs.LG TIER_1 English(EN) · Yingxu Wang, Xinwang Liu, Siyang Gao, Nan Yin · 2026-06-02 04:00

Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation

arXiv:2606.00808v1 Announce Type: new Abstract: Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer accessible. A central obstacle is pseudo-label reliability: under feature and topolog…