Snapchat/GiGL
Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks
This project helps machine learning engineers and data scientists build and deploy Graph Neural Networks (GNNs) for extremely large datasets, often involving billions of nodes. It takes raw graph data and task configurations as input, then outputs trained GNN models capable of performing tasks like node classification or link prediction at scale. The primary users are ML practitioners dealing with massive, interconnected datasets.
Available on PyPI.
Use this if you need to train and infer with Graph Neural Networks on datasets so large they require distributed processing, such as graphs with billions of connections or entities.
Not ideal if your datasets are small to medium-sized or if you are not familiar with configuring distributed machine learning infrastructure.
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Python
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Last pushed
Mar 13, 2026
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