The authors propose a new framework for making graph neural networks (GNNs) more transparent by moving complexity out of the model architecture and into the data itself. They "distill" a complex teacher GNN into an augmented graph with enriched features and structure, so that a simple student model can match the teacher's performance on this new graph. This lets humans inspect what the complex model was doing by looking at the augmented data rather than reverse-engineering opaque model internals.
Main takeaways:
- Standard GNN explainability methods highlight important nodes or edges for individual predictions, but don't explain why one architecture outperforms another.
- Model-to-Data (M2D) distillation transfers architectural advantages (like attention mechanisms or fairness constraints) into visible graph structure and features.
- A simple student GNN trained on the M2D-augmented graph matches the teacher's performance, making the learned behavior interpretable.
- The approach reveals mechanisms like attention-based aggregation by materializing them as changes in the graph topology or features.