This folder contains a plethora of examples covering different GNN use-cases. This readme highlights some key examples.

A great and simple example to start with is gcn.py, showing a user how to train a GCN model for node-level prediction on small-scale homogeneous data.

For a simple link prediction example, see link_pred.py.

For examples on Open Graph Benchmark datasets, see the ogbn_*.py examples:

• ogbn_products_sage.py and ogbn_products_gat.py show how to train GraphSAGE and GAT models on the ogbn-products dataset.
• ogbn_proteins_deepgcn.py is an example to showcase how to train deep GNNs on the ogbn-proteins dataset.
• ogbn_papers_100m.py is an example for training a GNN on the large-scale ogbn-papers100m dataset, containing approximately ~1.6B edges.
• ogbn_papers_100m_cugraph.py shows how to accelerate the ogbn-papers100m workflow using CuGraph.

For examples on using torch.compile, see the examples under examples/compile.

For examples on scaling PyG up via multi-GPUs, see the examples under examples/multi_gpu.

For examples on working with heterogeneous data, see the examples under examples/hetero.

For examples on co-training LLMs with GNNs, see the examples under examples/llm.