S²PLR identifies a safe subspace for reliable pseudo-labels in source-free graph domain adaptation using semantic committee signals and structural contrastive verification, then applies noise-tolerant regularization to uncertain samples.
Rank and align: Towards effec- tive source-free graph domain adaptation,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation
S²PLR identifies a safe subspace for reliable pseudo-labels in source-free graph domain adaptation using semantic committee signals and structural contrastive verification, then applies noise-tolerant regularization to uncertain samples.