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name: fair-graph-learning description: "Use DIG's fair graph learning stack for NBA/POKEC datasets, Graphair training and evaluation, and fairness-aware graph representation workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Fair Graph Learning

Use this sub-skill for DIG workflows around Graphair, fairness metrics, and the NBA/POKEC datasets.

Include

• dig.fairgraph.dataset: NBA, POKEC.
• dig.fairgraph.method: run, graphair, aug_module, GCN, GCN_Body, Classifier.
• dig.fairgraph.utils.utils: accuracy, fair_metric, scipysp_to_pytorchsp.
• Graphair training and evaluation workflows.

Exclude

• Graph augmentation without fairness: use ../graph-augmentation/SKILL.md.
• Molecular, 3D, SSL, explainability, GOOD, or large-scale graph workflows.

Start Here

• Read references/api-reference.md for dataset fields, helper metrics, and the runner class.
• Read references/workflows.md for the Graphair training/evaluation path.
• Read references/troubleshooting.md when .cuda() assumptions or data downloads become a problem.
• Run scripts/fairgraph_smoke.py for a safe metric-only smoke check.

Core Workflows

• Dataset preparation: instantiate NBA or POKEC, inspect sensitive attributes, and confirm the returned splits and metadata.
• Graphair training: use run() to construct the augmentation, encoder, adversary, and classifier stack, then train and evaluate.
• Fairness metrics: use accuracy and fair_metric for lightweight checks or reporting.