#python #deep_learning #geometric_deep_learning #graph_convolutional_networks #graph_neural_networks #pytorch
PyG (PyTorch Geometric) is a library that makes it easy to work with Graph Neural Networks (GNNs) using PyTorch. Here’s why it’s beneficial You can start training a GNN model with just 10-20 lines of code, especially if you're already familiar with PyTorch.
- **Comprehensive Models** The library supports large-scale graphs, dynamic graphs, and heterogeneous graphs, making it versatile for various applications.
- **Scalability** It provides extensive documentation, tutorials, and examples to help you get started quickly.
Overall, PyG simplifies the process of working with GNNs, making it a powerful tool for machine learning on graph-structured data.
https://github.com/pyg-team/pytorch_geometric
PyG (PyTorch Geometric) is a library that makes it easy to work with Graph Neural Networks (GNNs) using PyTorch. Here’s why it’s beneficial You can start training a GNN model with just 10-20 lines of code, especially if you're already familiar with PyTorch.
- **Comprehensive Models** The library supports large-scale graphs, dynamic graphs, and heterogeneous graphs, making it versatile for various applications.
- **Scalability** It provides extensive documentation, tutorials, and examples to help you get started quickly.
Overall, PyG simplifies the process of working with GNNs, making it a powerful tool for machine learning on graph-structured data.
https://github.com/pyg-team/pytorch_geometric
GitHub
GitHub - pyg-team/pytorch_geometric: Graph Neural Network Library for PyTorch
Graph Neural Network Library for PyTorch. Contribute to pyg-team/pytorch_geometric development by creating an account on GitHub.