<ul><li>The proposed framework for Graph Neural Networks (GNNs) learning has potential applications in various areas.</li><li>One application is in fair k-shot learning, which involves training a model to classify new examples based on a small number of labeled examples.</li><li>Another application is ensuring fair predictive performance of GNNs for specified structural groups by penalizing parameters with low distortion between structural distance and embedding distance.</li><li>Further research is needed to explore the actual application results of the framework.</li></ul>

Extending GNN Learning: 11 Additional Framework Applications

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