Model-to-Data Distillation for Graph Neural Networks

Excerpt

arXiv:2605.06814v2 Announce Type: replace Abstract: Graph neural networks (GNNs) increasingly rely on sophisticated architectures and training procedures to achieve desirable properties such as high predictive performance, fairness, and robustness. However, these properties typically remain tied to the models that learn them, limiting their transferability to simpler models and downstream settings. We introduce model-to-data (M2D) distillation, a new distillation paradigm that transfers properti