pytorch_geometric and deepsnap
PyTorch Geometric provides the core GNN primitives and layers, while DeepSnap builds on top of it as a higher-level library for converting graph data formats and integrating with PyTorch Geometric, making them complements rather than competitors.
About pytorch_geometric
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
This tool helps machine learning engineers and researchers build and train Graph Neural Networks (GNNs) for analyzing structured data. It takes graph-structured data (like social networks, molecular structures, or citation graphs) as input and produces trained GNN models capable of tasks such as classifying nodes, predicting links, or generating new graphs. It is designed for those already familiar with PyTorch.
About deepsnap
snap-stanford/deepsnap
Python library assists deep learning on graphs
This library helps machine learning engineers and researchers build and train deep learning models on graph-structured data more efficiently. It takes in graph data, such as social networks or molecular structures, and helps prepare it for training, then outputs trained graph neural network models. The primary users are Python developers working with graph deep learning frameworks.
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