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name: embedding-workflows description: "Guides StellarGraph unsupervised representation learning with Node2Vec, DeepWalk-style walks, Metapath2Vec, Attri2Vec, GraphSAGE unsupervised sampling, Deep Graph Infomax, GraphWave, and Watch Your Step." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Embedding Workflows

Use this sub-skill when the goal is to learn or extract node, edge, or graph embeddings before downstream clustering, visualization, classification, or link prediction.

Read first

• references/workflows.md for Node2Vec/DeepWalk, Metapath2Vec, Attri2Vec, unsupervised GraphSAGE, DGI, GraphWave, and Watch Your Step recipes.
• references/model-reference.md for verified constructors and generator pairings.
• references/troubleshooting.md for optional dependency, no-feature graph, random-walk, and embedding extraction issues.
• scripts/random_walk_smoke.py for a safe random-walk smoke without gensim training or downloads.

Route here when the user asks to

• produce Node2Vec/DeepWalk or Metapath2Vec walks/embeddings;
• use the StellarGraph Keras Node2Vec or Attri2Vec model classes;
• train unsupervised GraphSAGE using UnsupervisedSampler;
• wrap a base model with DeepGraphInfomax and extract embeddings;
• compute GraphWave or Watch Your Step embeddings;
• use embeddings in scikit-learn, visualization, or downstream link/node tasks.

Route elsewhere

• Random walk parameter debugging: ../sampling-generators/SKILL.md.
• Supervised node classification: ../node-classification-gnns/SKILL.md.
• Link prediction consuming embeddings: ../link-prediction-kg/SKILL.md.
• Graph classification using graph-level labels: ../graph-time-series-workflows/SKILL.md.

Operating workflow

1. Decide whether embeddings should use only graph structure, node attributes, heterogeneous metapaths, or a GNN encoder.
2. Validate graph IDs and features. Structure-only walks can work without node features; Attri2Vec, GraphSAGE, DGI base models, and many GNN encoders require numeric node features.
3. Choose the route:
   • Node2Vec/DeepWalk: random walks plus external Word2Vec/Gensim or the Keras Node2Vec path;
   • Metapath2Vec: heterogeneous metapath walks plus external Word2Vec/Gensim;
   • Attri2Vec: Attri2VecNodeGenerator/Attri2VecLinkGenerator and Attri2Vec;
   • unsupervised GraphSAGE: UnsupervisedSampler + GraphSAGELinkGenerator + GraphSAGE;
   • DGI: base GCN/GAT/GraphSAGE/HinSAGE model plus DeepGraphInfomax;
   • GraphWave: GraphWaveGenerator for structural embeddings;
   • Watch Your Step: AdjacencyPowerGenerator and WatchYourStep.
4. Keep embedding extraction separate from downstream evaluation. Save or pass a DataFrame/array indexed by node IDs so downstream tasks do not lose identity.

Safe check

python sub-skills/embedding-workflows/scripts/random_walk_smoke.py --help
python sub-skills/embedding-workflows/scripts/random_walk_smoke.py

The smoke verifies random-walk generation and does not train Word2Vec or a neural model.