Using geometric properties like Ollivier-Ricci curvature to guide knowledge distillation helps MLPs capture the graph structure that GNNs learn, improving accuracy while keeping deployment simple.
This paper addresses the challenge of distilling knowledge from Graph Neural Networks (GNNs) to simpler MLPs for deployment. The authors identify two spectral failure modes—underfit on sparse graphs and overfit on dense graphs—and propose G²MLP, which uses Ollivier-Ricci curvature to guide where the student MLP should preserve the teacher's geometric structure.