Label-guided graph contrastive learning for semi-supervised node classification

Meixin Peng, Xin Juan, Zhanshan Li

Abstract: Highlights•The framework explores semantic-level feature similarity.•The self-checking mechanism ensures the authenticity of the positive nodes.•The reweighting strategy enhances the effect of hard negative nodes.•The training algorithm exhibits competitive performance on various datasets.