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# Examples
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`](./gcn.py), showing a user how to train a [`GCN`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.models.GCN.html) model for node-level prediction on small-scale homogeneous data.
For a simple link prediction example, see [`link_pred.py`](./link_pred.py).
For examples on [Open Graph Benchmark](https://ogb.stanford.edu/) datasets, see the `ogbn_*.py` examples:
- [`ogbn_products_sage.py`](./ogbn_products_sage.py) and [`ogbn_products_gat.py`](./ogbn_products_gat.py) show how to train [`GraphSAGE`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.models.GraphSAGE.html) and [`GAT`](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.models.GAT.html) models on the `ogbn-products` dataset.
- [`ogbn_proteins_deepgcn.py`](./ogbn_proteins_deepgcn.py) is an example to showcase how to train deep GNNs on the `ogbn-proteins` dataset.
- [`ogbn_papers_100m.py`](./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`](./ogbn_papers_100m_cugraph.py) shows how to accelerate the `ogbn-papers100m` workflow using [CuGraph](https://github.com/rapidsai/cugraph).
For examples on using `torch.compile`, see the examples under [`examples/compile`](./compile).
For examples on scaling PyG up via multi-GPUs, see the examples under [`examples/multi_gpu`](./multi_gpu).
For examples on working with heterogeneous data, see the examples under [`examples/hetero`](./hetero).
For examples on co-training LLMs with GNNs, see the examples under [`examples/llm`](./llm).
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