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name: stellargraph description: "Routes StellarGraph graph machine learning tasks across graph construction, samplers, TensorFlow Keras models, link prediction, embeddings, graph classification, time series, calibration, ensembles, interpretability, datasets, and Neo4j connector workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

StellarGraph Repo Skill

Use this skill when a task involves the StellarGraph Python package or its TensorFlow/Keras graph machine learning workflows. It is a router: read the smallest sub-skill that matches the user's task, then use the bundled references and scripts from that sub-skill.

First checks

• StellarGraph's package metadata targets Python >=3.6,<3.9; prefer a Python 3.8 environment when installing or reproducing old examples.
• Basic install for package use: python -m pip install stellargraph.
• Use python -m pip install "stellargraph[demos]" only when notebook-style demo dependencies such as gensim, rdflib, numba, Jupyter, or plotting helpers are needed.
• Use python -m pip install "stellargraph[neo4j]" only for Neo4j connector workflows; it still requires a running Neo4j service.
• Run scripts/check_stellargraph_environment.py when you need a safe import, TensorFlow/Keras, graph-construction, and optional backend diagnostic before following a workflow.
• Read references/troubleshooting.md for Python/TensorFlow/import, optional dependency, dataset-cache, GPU, and Neo4j failures that affect multiple workflows.
• Read references/repo-provenance.md before deciding whether this skill is stale for a newer checkout or package version.

Route by task

Data, graph objects, and datasets

Read sub-skills/graph-data-loading/SKILL.md when the task is about:

• constructing StellarGraph or StellarDiGraph objects from Pandas, NumPy, IndexedArray, or NetworkX;
• choosing node/edge IDs, types, features, weights, or directedness;
• converting/querying graph structure or checking graph readiness for ML;
• using built-in dataset loader classes such as Cora, MovieLens, WN18, or METR_LA without confusing cache/download behavior with model logic.

Walkers, samplers, and Keras generators

Read sub-skills/sampling-generators/SKILL.md when the task is about random walks, sampled breadth-first neighborhoods, UnsupervisedSampler, full-batch, sampled mini-batch, padded-graph, knowledge-graph, or sliding-window generators.

Use this route before model routes when the failure mentions flow, Sequence, num_samples, n_size, node/link IDs, batch dimensions, sparse adjacency tensors, or generator/model incompatibility.

Node classification or node regression with GNNs

Read sub-skills/node-classification-gnns/SKILL.md for supervised or semi-supervised node workflows using GCN, GAT, GraphSAGE, DirectedGraphSAGE, HinSAGE, ClusterNodeGenerator + GCN, RGCN, PPNP, APPNP, or the SGC-style linear GCN pattern.

Use sibling routes for upstream graph construction and generator details; this route owns model selection, Keras tensor wiring, targets, and training/evaluation shape decisions for node tasks.

Link prediction, link regression, and knowledge graphs

Read sub-skills/link-prediction-kg/SKILL.md for edge-split workflows, GraphSAGELinkGenerator, HinSAGELinkGenerator, FullBatchLinkGenerator, link inference heads, temporal link prediction, and knowledge graph completion with KGTripleGenerator, ComplEx, DistMult, RotatE, RotE, or RotH.

Unsupervised embeddings and representation learning

Read sub-skills/embedding-workflows/SKILL.md for Node2Vec/DeepWalk, Metapath2Vec, Attri2Vec, unsupervised GraphSAGE, Deep Graph Infomax, GraphWave, Watch Your Step, or extracting embeddings for scikit-learn/Keras downstream models.

Graph classification and graph time series

Read sub-skills/graph-time-series-workflows/SKILL.md for graph-level classification with PaddedGraphGenerator, GCNSupervisedGraphClassification, DeepGraphCNN, or SortPooling, and for spatio-temporal forecasting with SlidingFeaturesNodeGenerator, fixed adjacency, and GCN_LSTM.

Model operations, interpretability, and Neo4j

Read sub-skills/model-ops-interpretability/SKILL.md for:

• custom_keras_layers when saving/loading Keras models that contain StellarGraph layers;
• calibration (TemperatureCalibration, IsotonicCalibration, expected calibration error, reliability plots);
• Ensemble and BaggingEnsemble wrappers;
• GCN/GAT saliency and integrated-gradient interpretability;
• optional Neo4j connector objects and service-backed generators.

Package map

Read references/package-overview.md when a user names an algorithm, dataset, module, notebook title, or capability and you need to pick the correct route before acting.

Boundaries

• This skill teaches package operation, not repository release maintenance or CI pipeline editing.
• Do not run long notebook demos, dataset downloads, Neo4j services, or GPU checks unless the user explicitly wants that external dependency or runtime.
• Prefer safe bundled scripts and tiny synthetic fixtures before attempting full notebook-scale examples.