link-property-prediction

Link Property Prediction

Use this subskill for the link-property family: ogbl-* datasets, ranking metrics, and knowledge-graph completion workflows.

Use this subskill when

• The task names LinkPropPredDataset, PygLinkPropPredDataset, DglLinkPropPredDataset, or ogbl-*.
• The task asks for hits@K, mrr, or link-level rocauc evaluation.
• The task mentions ogbl-collab, ogbl-ppa, ogbl-citation2, ogbl-ddi, ogbl-biokg, ogbl-wikikg2, or ogbl-vessel.

First decisions

1. Read references/workflows.md for the link prediction flow and metric-specific shapes.
2. Read references/api-reference.md for the public class names, split structure, and evaluator inputs.
3. Read references/troubleshooting.md when ranking shapes or top-k arrays fail validation.

Main workflow

• Pick the exact ogbl-* dataset name.
• Load the dataset and inspect get_edge_split().
• Decide whether the task uses hits@K, MRR, or ROC-AUC.
• Keep the positive and negative score shapes aligned before calling the evaluator.
• For knowledge-graph completion, pay attention to the rank-based inputs rather than treating the task like ordinary binary edge classification.

Common routing choices

• ogbl-collab / ogbl-ppa -> graph link prediction baselines.
• ogbl-citation2 -> citation ranking with MRR-style evaluation.
• ogbl-biokg / ogbl-wikikg2 -> knowledge-graph completion.
• ogbl-ddi -> edge prediction with hits@20.
• ogbl-vessel -> ROC-AUC with the external contribution workflow.

Optional backend note

PyG and DGL wrappers are optional. If they are not installed, the core OGB link workflow still works through the library-agnostic loader.

What not to do here

• Do not treat link prediction as a node-classification task.
• Do not feed score tensors with the wrong rank into the evaluator.
• Do not depend on the original checkout for final runtime guidance.

Related references

• ../../references/api-overview.md
• ../../references/dataset-catalog.md
• ../../references/troubleshooting.md