Research map / Graph learning / GNN architectures
GNNExplainer: Generating Explanations for Graph Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GNNExplainer to identify predictive subgraphs and node features | GNN predictions on synthetic graphs with planted motifs · Synthetic graphs with planted motifs | explanation accuracy: up to 43.0% | Helped |
Closest papers by meaning
- GnnXemplar: Exemplars to Explanations - Natural Language Rules for Global GNN Interpretability (NeurIPS 2025 · similarity 0.92)
- EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time (ICML 2024 · similarity 0.92)
- GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules (NeurIPS 2024 · similarity 0.91)
- Self-Interpretable Graph Learning with Sufficient and Necessary Explanations (AAAI 2024 · similarity 0.91)
- Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks (ICLR 2024 · similarity 0.91)
- Higher Order Structures for Graph Explanations (AAAI 2025 · similarity 0.91)
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