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name: graph-property-prediction description: "Routes OGB graph-property workflows for ogbg datasets, molecular graphs, and code2 conversion." metadata: disco-role: operating disable-model-invocation: true license: MIT

Graph Property Prediction

Use this subskill for the graph-property family: ogbg-* datasets, molecular classification/regression, ogbg-code2, ogbg-ppa, and SMILES-to-graph conversion.

Use this subskill when

• The task names GraphPropPredDataset, PygGraphPropPredDataset, DglGraphPropPredDataset, or ogbg-*.
• The task asks for graph-level evaluation metrics such as ROC-AUC, average precision, RMSE, accuracy, or code2 F1.
• The task mentions smiles2graph, py2graph, molecular datasets, or code to graph conversion.
• The task needs the PyG or DGL graph-property wrappers and the corresponding backend packages are installed.

First decisions

1. Read references/workflows.md for the graph property task flow and dataset-specific gotchas.
2. Read references/api-reference.md for the public class names, input/output shapes, and metric names.
3. Read references/troubleshooting.md when a loader, evaluator, or conversion helper fails.
4. If you need a tiny sanity check for code2-style AST conversion, run scripts/py2graph-smoke.py.

Main workflow

• Pick the dataset name from the exact ogbg-* catalog.
• Decide whether you need the library-agnostic loader or the PyG/DGL wrapper.
• Load the dataset and inspect get_idx_split().
• Read the evaluator's expected input format before computing metrics.
• For molecular datasets, convert external molecules with smiles2graph rather than hand-assembling graph dictionaries.
• For ogbg-code2, treat the evaluator input as token sequences, not numeric labels.

Common routing choices

• ogbg-mol* -> graph classification/regression with molecule graphs.
• ogbg-ppa -> graph classification with accuracy.
• ogbg-code2 -> code-to-graph conversion and F1-style sequence evaluation.
• smiles2graph -> molecule-to-graph helper backed by rdkit.

Optional backend note

PyG and DGL wrappers are optional. If the current environment does not have the matching backend packages, route the task to the library-agnostic loader or ask for the optional dependency rather than assuming the graph-property skill is broken.

What not to do here

• Do not route node/link/LSC dataset questions into this subskill just because they are graph-shaped.
• Do not tell the user to run the original repository's training scripts as the runtime answer.
• Do not depend on the original checkout for the final skill content.

Related references

• ../../references/api-overview.md
• ../../references/dataset-catalog.md
• ../../references/troubleshooting.md
• ../../scripts/smiles2graph-smoke.py