#other #graph_convolutional_networks #graph_neural_networks #graph_representation_learning #scalable_deep_learning #gnn #efficient_neural_networks #efficient_deep_learning

https://github.com/chaitjo/awesome-efficient-gnn

#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