CalibHyper: Chance-Corrected Relational Hypergraphs for Few-Shot Molecular Property Prediction
This study addresses the challenges of label scarcity, sensitivity of label consistency to class distributions, and difficulty in capturing attribute dependencies in molecular property prediction. We propose an opportunity-corrected relational hypergraph method based on joint label distributions. Specifically, this approach removes the independence baseline via subtraction and shrinks the residual, employing a permutation-equivariant head to estimate residuals for adaptively selecting auxiliary attributes and assigning hyperedge weights. By integrating relational hypergraphs with permutation-equivariant networks, it directly models inter-attribute dependencies. Extensive experiments across thirteen datasets from five benchmarks demonstrate that the proposed method achieves ROC-AUC performance comparable to state-of-the-art results under both 1-shot and 10-shot settings.