RexYing/gnn-model-explainer

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1055 stars · 193 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official implementation of GNNExplainer, a NeurIPS 2019 method for explaining predictions of graph neural networks by learning soft masks over edges and node features. It includes training scripts for GCN models, the explainer optimization, and visualization via Tensorboard, Jupyter notebooks, and D3.js export.

Use cases

• explain predictions of a graph neural network
• identify important edges and node features for a GNN prediction
• replicate GNNExplainer paper experiments
• visualize GNN explanations interactively
• interpret node classification results on graphs
• generate subgraph explanations for GCN models

When to choose

• you need model-agnostic explanations for PyTorch Geometric-style GCN predictions
• you want to reproduce the original GNNExplainer paper results
• you are researching explainability methods for graph neural networks

When to avoid

• you need explanations for non-graph models like transformers or CNNs
• you want a maintained production library with broad model support (use PyTorch Geometric's Explainer instead)
• you need GNN explanation for frameworks other than PyTorch

Facets

library · maturity maintenance

machine-learning deep-learning data-visualization interpreter machine-learning deep-learning graph-processing data-visualization python cross-platform gnn explainability graph-neural-networks interpretability xai neurips-2019 research-code research

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