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Found 2 papers, 2 papers with code
HomoGCL: Rethinking Homophily in Graph Contrastive Learning
Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting" learning scheme.
GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification
Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios.
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