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name: link-prediction-kg description: "Guides StellarGraph link prediction, link regression, edge splitting, temporal link workflows, and knowledge graph completion with ComplEx, DistMult, RotatE, RotE, and RotH." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Link Prediction and Knowledge Graphs

Use this sub-skill when a StellarGraph task predicts edges/ratings/relations, performs link regression/classification, splits graph edges into train/test sets, or scores triples in a knowledge graph.

Read first

• references/link-workflows.md for edge splitting, link generators, Keras link heads, temporal link prediction, and leakage checks.
• references/knowledge-graphs.md for KGTripleGenerator, ComplEx, DistMult, RotatE, RotE, RotH, negative sampling, and ranking.
• references/api-reference.md for verified signatures and link/KG object responsibilities.
• references/troubleshooting.md for edge leakage, invalid endpoints, target-shape, negative sampling, and relation-type failures.
• scripts/link_prediction_smoke.py for a tiny GCN link-classification wiring check.

Route here when the user asks to

• use EdgeSplitter.train_test_split;
• build link prediction with GraphSAGELinkGenerator, HinSAGELinkGenerator, FullBatchLinkGenerator, Attri2VecLinkGenerator, or Node2VecLinkGenerator;
• add link_classification, link_regression, or link_inference to source and destination node embeddings;
• perform knowledge graph completion with KGTripleGenerator, ComplEx, DistMult, RotatE, RotE, or RotH;
• use SelfAdversarialNegativeSampling or rank triples against all nodes;
• adapt CTDNE or temporal link-prediction workflows.

Route elsewhere

• Raw graph and dataset construction: ../graph-data-loading/SKILL.md.
• Generator shape debugging: ../sampling-generators/SKILL.md.
• Node classification: ../node-classification-gnns/SKILL.md.
• Node embeddings before downstream edge tasks: ../embedding-workflows/SKILL.md.

Operating workflow

1. Construct a graph with valid node IDs, edge IDs/types, and features required by the chosen embedding model.
2. Split edges for evaluation with EdgeSplitter or an equivalent leakage-safe method. Keep train and test positives/negatives separate.
3. Choose a link generator:
   • GCN/GAT/PPNP-style full-batch embeddings: FullBatchLinkGenerator;
   • GraphSAGE: GraphSAGELinkGenerator;
   • HinSAGE: HinSAGELinkGenerator with head node types;
   • Attri2Vec/Node2Vec link heads: matching link generators;
   • knowledge graph completion: KGTripleGenerator.
4. Build the base embedding model and call in_out_tensors().
5. Attach a link head such as link_classification, link_regression, or link_inference to combine source/destination embeddings.
6. Compile and train/evaluate with generator flow(link_ids, targets, ...).
7. For knowledge graph models, use negative sampling and ranking routines designed for triples rather than ordinary binary edge pairs.

Safe check

python sub-skills/link-prediction-kg/scripts/link_prediction_smoke.py --help
python sub-skills/link-prediction-kg/scripts/link_prediction_smoke.py

The smoke uses a tiny graph and does not download datasets or train a notebook.