stellargraph/stellargraph

Health v2 · maintenance only

Adoption not part of the score

3060 stars · 443 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

StellarGraph is a Python library for machine learning on graphs and networks, offering state-of-the-art graph neural network algorithms such as GCN, GraphSAGE, and heterogeneous graph models. It supports node classification, link prediction, graph classification, and representation learning on graph-structured data.

Use cases

• classify nodes in a graph with graph neural networks
• predict missing links in a network
• learn node embeddings for downstream machine learning
• run machine learning on heterogeneous graphs
• train a GCN on graph-structured data
• visualize graph embeddings
• graph classification with deep learning

When to choose

• you need graph machine learning algorithms in Python with a Keras/TensorFlow backend
• you work with heterogeneous graphs or attributed networks
• you want node classification, link prediction, or graph embeddings out of the box
• your data is already in NetworkX or pandas graph form

When to avoid

• you need a PyTorch-based GNN library (consider PyTorch Geometric or DGL instead)
• you need actively developed features - the project is in maintenance mode
• your data is not graph-structured
• you need very large-scale distributed graph training

Facets

library · maturity maintenance

machine-learning deep-learning data-science machine-learning deep-learning data-science graph-processing python graph-neural-networks gcn link-prediction node-classification heterogeneous-graphs graph-embeddings networkx tensorflow

Member repositories

For agents

markdown · JSON · MCP: product_card(name="stellargraph/stellargraph")