Distilling GNN Knowledge into Non-Neural Cell Graph Student Models

Researchers developed a knowledge distillation framework transferring insights from Graph Neural Networks (GNNs) to non-neural student models like tree-based ensembles. Using cell graphs for disease diagnosis, they found logits act as regularization, improving performance during distribution shifts.