The proposed framework demonstrates applicability in various GNN learning paradigms, particularly in fairness for k-shot learning and ensuring equitable predictive performance across structural groups.
In k-shot learning, limited labeled data can still facilitate effective training, especially in scenarios requiring quick classification based on few examples; this boosts model robustness.
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