microsoft/gated-graph-neural-network-samples resource

Health v2 · maintenance only

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1029 stars · 255 forks observed · 2026-08-28

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

Sample implementations of Gated Graph Neural Networks (GGNN) and Graph Convolutional Networks in TensorFlow, applied to learning properties of chemical molecules. It includes dense, sparse, and asynchronous GNN variants, and is no longer maintained in favor of microsoft/tf-gnn-samples.

Use cases

• learn how to implement gated graph neural networks
• train a GNN to predict chemical molecule properties
• compare dense vs sparse graph neural network implementations
• run graph convolutional networks on molecular data
• study asynchronous message passing in graph neural networks

When to choose

• you want reference sample code for GGNNs in TensorFlow 1.x
• you are studying molecular property prediction with GNNs
• you need a starting point for dense or sparse GNN implementations

When to avoid

• you need maintained or up-to-date GNN code - use microsoft/tf-gnn-samples instead
• you use modern TensorFlow or PyTorch versions
• you need production-ready GNN libraries

Facets

learning-resource · maturity abandoned

machine-learning deep-learning machine-learning deep-learning chemistry python graph-neural-networks tensorflow sample-code ggnn graph-convolutional-networks molecular-property-prediction

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