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Found 3 papers, 3 papers with code
Semi-Supervised Graph Imbalanced Regression
The training data balance is achieved by (1) pseudo-labeling more graphs for under-represented labels with a novel regression confidence measurement and (2) augmenting graph examples in latent space for remaining rare labels after data balancing with pseudo-labels.
Data-Centric Learning from Unlabeled Graphs with Diffusion Model
A conventional approach is training a model with the unlabeled graphs on self-supervised tasks and then fine-tuning the model on the prediction tasks.
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