---

name: graph-explainability description: "Use DIG's xgraph stack for subgraph, edge, node, walk, and saliency explanations with SubgraphX, PGExplainer, GNNExplainer, GNN-LRP, DeepLIFT, GradCAM, FlowX, and explanation metrics." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Graph Explainability

Use this sub-skill for DIG workflows that explain a trained GNN or evaluate explanation metrics.

Include

• dig.xgraph.dataset: BA_LRP, MarginalSubgraphDataset, MoleculeDataset, SentiGraphDataset, SynGraphDataset.
• dig.xgraph.method: DeepLIFT, FlowX, GNNExplainer, GNN_GI, GNN_LRP, GradCAM, PGExplainer, SubgraphX, MCTS.
• dig.xgraph.evaluation: XCollector, ExplanationProcessor, control_sparsity.
• dig.xgraph.models: GCN_2l, GIN_2l, load_model, and the checkpoint compatibility helpers in dig.xgraph.utils.compatibility.
• Benchmark-style xgraph workflows using the bundled benchmarks/xgraph source evidence.

Exclude

• Training new base GNNs from scratch: use the SSL or model-specific sub-skill that owns the model.
• 2D/3D molecular generation, OOD datasets, augmentation, fairness, or large-scale loaders: route elsewhere.

Start Here

• Read references/api-reference.md for the explainers, metrics, and compatibility helpers.
• Read references/workflows.md for node, graph, and benchmark workflows.
• Read references/troubleshooting.md for checkpoint, mask, and model-config issues.
• Run scripts/xgraph_metric_smoke.py for a safe CPU-only metric smoke check.

Core Workflows

• Node or graph explanation: build a trained model, choose an explainer, and request an explanation for a node, graph, or edge target.
• Subgraph explanations: use SubgraphX/MCTS when the question is which subgraph matters most.
• Edge-mask metrics: use ExplanationProcessor with XCollector and control_sparsity to compute fidelity and sparsity.
• Checkpoint compatibility: adapt saved model state dicts with compatible_state_dict when PyG versions change.

Quick Validation

python scripts/xgraph_metric_smoke.py --help
python scripts/xgraph_metric_smoke.py