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Layer-wise contrastive network for unsupervised graph representation learning
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DOI：10.1016/j.neucom.2026.132736.png)
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Abstract
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• We introduce a novel contrastive loss to learn graph representations by contrasting shallow and deep features. • Our method is flexible and can be readily combined with existing graph contrastive learning techniques that utilize data augmentation. • We demonstrate outstanding performance with our method across four benchmark datasets for node classification.
Keywords:
Graph representation learning
Contrastive learning
Unsupervised learning
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