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Found 4 papers, 3 papers with code
Normalize Then Propagate: Efficient Homophilous Regularization for Few-shot Semi-Supervised Node Classification
In this paper, we first analyze the restrictions of GNNs generalization from the perspective of supervision signals in the context of few-shot semi-supervised node classification.
Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
Next, we propose a clean labels oriented link that connects unlabeled nodes to cleanly labeled nodes, aimed at mitigating label sparsity and promoting supervision propagation.
Similarity-Navigated Conformal Prediction for Graph Neural Networks
In this paper, we empirically show that for each node, aggregating the non-conformity scores of nodes with the same label can improve the efficiency of conformal prediction sets while maintaining valid marginal coverage.
TorchCP: A Python Library for Conformal Prediction
Conformal Prediction (CP) has attracted great attention from the research community due to its strict theoretical guarantees.
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