GraphSAGE

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

3719 stars · 855 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Reference implementation of the GraphSAGE algorithm for inductive representation learning on large graphs using stochastic graph convolutions. It generates node embeddings for massive graphs with rich feature information, including a simpler PyTorch variant.

Use cases

• generate embeddings for nodes in large graphs
• train inductive node classifiers on graphs with new unseen nodes
• run graph convolutional representation learning on graphs with over 100k nodes
• learn embeddings for graphs without node features using identity features
• reproduce results from the GraphSAGE NIPS 2017 paper

When to choose

• you need inductive node embeddings on very large graphs (>100k nodes)
• you want the original reference implementation of the GraphSAGE algorithm
• your graph has rich node feature information and is dynamic

When to avoid

• your graph is small or static, where subsampling overhead outweighs benefits
• you need a maintained production library with modern framework support
• you cannot work with pinned old dependencies like networkx <=1.11 and legacy TensorFlow

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning python graph-neural-networks graph-embeddings node-embeddings inductive-learning tensorflow research-code algorithms

Member repositories

For agents

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