Skills-SIMarket
skills-si.com
Proposer une Skill
/
L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Données et recherche · SKILL.md

link-property-prediction

link-property-prediction

Routes OGB link-prediction workflows for ogbl datasets, ranking metrics, and KG completion.

Independent GitHub snapshot · Automated classification · Original documentation governs

Vvectorspacelab / arex-skillApache-2.0

Documentation de la Skill

GitHub

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

  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

More skills to explore

Voir toutes les Skills
Skills enregistrées