tensorflow/gnn

Health v2 · maintenance only

Adoption not part of the score

1541 stars · 203 forks observed · 2026-08-28

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

TensorFlow GNN is a Python library for building Graph Neural Networks on TensorFlow, including a GraphTensor type for heterogeneous graphs, graph sampling tools, and ready-to-use GNN models. It is an open-source port of a Google-internal library used for scalable graph learning.

Use cases

• train graph neural networks on heterogeneous graphs
• classify molecular graphs with GNNs
• sample subgraphs from a large graph database for training
• run node classification on citation networks like OGBN-MAG
• predict shortest paths with graph networks
• build custom GNN models with Keras layers

When to choose

• you want to build GNNs within the TensorFlow/Keras 2 ecosystem
• you need to model heterogeneous graphs with multiple node and edge types
• you need scalable graph sampling from large graph databases
• you want ready-made GNN architectures plus a training runner API

When to avoid

• your project uses Keras 3 or multi-backend Keras
• you prefer PyTorch-based graph learning (consider PyG or DGL instead)
• your data is not naturally graph-structured
• you need a framework-agnostic GNN solution

Facets

library · maturity active

machine-learning deep-learning etl machine-learning deep-learning data-science python graph-neural-networks tensorflow keras heterogeneous-graphs graph-sampling gpu

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