G2MLP stratifies distillation supervision by Ollivier–Ricci curvature, achieving the best graph-free student accuracy on all six node-classification benchmarks and a 4.02-point gain on Citeseer
The authors propose G2MLP, a training-time GNN-to-MLP distillation framework that uses Ollivier–Ricci curvature to allocate supervision between prediction-level and representation-level alignment, correcting spectral underfit on sparse graphs and spectral overfit on dense graphs; the graph-free MLP student achieves the best accuracy on all six node-classification benchmarks and transfers without architectural change to Graph Transformer teachers and link prediction.