snap-stanford/pretrain-gnns resource

Health v2 · maintenance only

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1070 stars · 173 forks observed · 2026-08-28

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

A PyTorch research codebase implementing strategies for pre-training graph neural networks from the ICLR 2020 paper by Stanford SNAP. It provides self-supervised and supervised pre-training methods plus fine-tuning scripts for chemistry and biology datasets.

Use cases

• pre-train graph neural networks on molecular datasets
• reproduce ICLR 2020 GNN pre-training paper experiments
• fine-tune pre-trained GNN models on downstream chemistry tasks
• compare self-supervised pre-training strategies for GNNs
• learn graph representation learning techniques
• apply pre-trained GNNs to biology datasets

When to choose

• you want to reproduce or build on the Strategies for Pre-training GNNs paper
• you need pre-trained GIN models for molecular or biological graph tasks
• you are studying transfer learning for graph neural networks

When to avoid

• you need a production-ready or maintained GNN library
• you want a general-purpose graph learning framework rather than paper code
• you need support for recent PyTorch or PyTorch Geometric versions

Facets

learning-resource · maturity maintenance

machine-learning deep-learning machine-learning deep-learning chemistry bioinformatics python graph-neural-networks pre-training pytorch self-supervised-learning gnn research-code transfer-learning

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markdown · JSON · MCP: product_card(name="snap-stanford/pretrain-gnns")