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Discover a framework that enhances GNN learning, enabling fair k-shot learning and fairness constraint for equitable predictive performance in structural groups

This appendix details the experimental setup, hardware, hyperparameters, and additional results confirming GNN performance and findings across datasets.

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Modified greedy and sampling algorithms solve Eq. (6) in GNNs for the k-center problem, with a running time complexity of O(k) and O(kn) respectively.

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Learn how GNNs exhibit unfair generalization when comparing test groups with significant structural distance differences.

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