alibaba/graph-learn

Health v2 · maintenance only

Adoption not part of the score

1341 stars · 266 forks observed · 2026-08-28

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

Graph-Learn (formerly AliGraph) is a distributed framework for developing and applying large-scale graph neural networks, with a training library compatible with TensorFlow and PyTorch and a dynamic graph online inference service. It provides Python and C++ graph sampling interfaces with a gremlin-like GSL, and is used in production at Alibaba for search recommendation, security, and knowledge graphs.

Use cases

• train graph neural networks on large-scale graphs
• distributed GNN model training with TensorFlow or PyTorch
• real-time graph sampling for online GNN inference
• incremental training on streaming dynamic graphs
• build recommendation models using graph embeddings
• run GNN models for fraud and network security detection
• serve GNN predictions with low P99 latency

When to choose

• you need industrial-scale distributed GNN training
• your graph data updates in real time and you need online inference
• you want a sampling framework compatible with both TensorFlow and PyTorch
• you need low-latency sampling guarantees for serving GNNs

When to avoid

• you only need small-scale graph analytics without neural networks
• you want a pure PyTorch-native GNN library without a distributed service layer
• your project needs Windows or macOS support
• you need a lightweight library for simple graph algorithms

Facets

framework · maturity active

machine-learning deep-learning llm-training rag machine-learning deep-learning large-language-models microservices python cpp gnn graph-neural-networks graph-sampling tensorflow pytorch dynamic-graph graph-inference alibaba linux docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="alibaba/graph-learn")