---

name: graph-augmentation description: "Use DIG's graph augmentation stack for GraphAug, S-Mixup, augmentation configs, degree transforms, subset/triplet datasets, and augmentation-driven graph classification workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Graph Augmentation

Use this sub-skill for DIG workflows that learn or apply graph augmentations.

Include

• dig.auggraph.dataset: DegreeTrans, AUG_trans, Subset, TripleSet.
• dig.auggraph.method.GraphAug: RunnerAugCls, RunnerGenerator, RunnerRewardGen.
• dig.auggraph.method.SMixup: smixup.
• GraphAug configuration enums and constants for dataset names, augmentation types, and model types.

Exclude

• Fairness workflows that rely on Graphair: use ../fair-graph-learning/SKILL.md.
• Molecular, 3D, SSL, explainability, GOOD, or large-scale loader workflows.

Start Here

• Read references/api-reference.md for the public classes and config constants.
• Read references/workflows.md for the three GraphAug runner flows and S-Mixup.
• Read references/troubleshooting.md when CUDA, TU datasets, or label layouts are problematic.
• Run scripts/augmentation_config_smoke.py for a safe import-and-transform smoke check.

Core Workflows

• Reward generator training: train a label-discriminating reward model on TU datasets.
• Augmentation generator training: use a trained reward model to update the augmentation generator.
• Augmented classifier training: combine the generator with a classifier to evaluate the augmentation policy.
• S-Mixup: compute soft alignments and mix graph pairs for graph classification.