Model-to-Data Distillation for Graph Neural Networks
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 properties learned by a complex GNN teacher into graph data, enabling simpler models to recover them through standard training. M2D distillation explicitly trades model complexity for data complexity by jointly learning augmented node features and graph str...
| Authors | Debolina Halder Lina, Arlei Silva |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 30 September 2026 |
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