name: link-property-prediction
description: "Routes OGB link-prediction workflows for ogbl datasets, ranking
metrics, and KG completion."
metadata:
disco-role: operating
disable-model-invocation: true
license: MIT
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
- Read
references/workflows.md for the link
prediction flow and metric-specific shapes.
- Read
references/api-reference.md for the
public class names, split structure, and evaluator inputs.
- 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