Reasons to use PyG:

• Easy-to-use and unified API: All it takes is 10-20 lines of code to get started with training a GNN model. PyG is PyTorch-on-the-rocks: It utilizes a tensor-centric API and keeps design principles close to vanilla PyTorch.

• Comprehensive and well-maintained GNN models: Most of the state-of-the-art Graph Neural Network architectures have been implemented by library developers or authors of research papers and are ready to be applied.

• Great flexibility: Existing PyG models can easily be extended for conducting your own research with GNNs. Making modifications to existing models or creating new architectures is simple, thanks to its easy-to-use message passing API, and a variety of operators and utility functions.

• Large-scale real-world GNN models: We focus on the need of GNN applications in challenging real-world scenarios, and support learning on diverse types of graphs, including but not limited to: scalable GNNs for graphs with millions of nodes; dynamic GNNs for node predictions over time; heterogeneous GNNs with multiple node types and edge types.

• GraphGym integration: GraphGym lets users easily reproduce GNN experiments, is able to launch and analyze thousands of different GNN configurations, and is customizable by registering new modules to a GNN learning pipeline.