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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 GNN based link prediction example, see [`link_pred.py`](./link_pred.py).
For an improved GNN based link prediction approach using Attract-Repel embeddings that can significantly boost accuracy (up to 23% improvement in AUC), see [`ar_link_pred.py`](./ar_link_pred.py). This approach is based on [Pseudo-Euclidean Attract-Repel Embeddings for Undirected Graphs](https://arxiv.org/abs/2106.09671).
To see an example for doing link prediction with an advanced Graph Transformer called [`LPFormer`](https://arxiv.org/abs/2310.11009), see \[`lpformer.py`\].
For examples on [Open Graph Benchmark](https://ogb.stanford.edu/) datasets, see the `ogbn_*.py` examples:
- [`ogbn_train.py`](./ogbn_train.py) is an example for training a GNN on the large-scale `ogbn-papers100m` dataset, containing approximately ~1.6B edges or the medium scale `ogbn-products` dataset, ~62M edges.
- Uses SGFormer (a kind of GraphTransformer) by default.
- [SGFormer Paper](https://arxiv.org/pdf/2306.10759)
- [Polynormer](https://arxiv.org/pdf/2403.01232)
- [Kumo.ai x NVIDIA x Stanford Graph Transformer Webinar](https://www.youtube.com/watch?v=wAYryx3GjLw)
- [`ogbn_proteins_deepgcn.py`](./ogbn_proteins_deepgcn.py) is an example to showcase how to train deep GNNs on the `ogbn-proteins` dataset.
- [`ogbn_train_cugraph.py`](./ogbn_train_cugraph.py) shows how to accelerate the `ogbn_train.py` workflow using [CuGraph](https://github.com/rapidsai/cugraph).
- [`ogbn_train_perforatedai.py`](https://github.com/PerforatedAI/PerforatedAI-Examples/tree/master/otherExamples/torch_geometric/OGBNProducts) shows how to optimize the `ogbn_train.py` workflow using [Perforated AI](https://github.com/PerforatedAI/PerforatedAI-API). Perforated AI provides a PyTorch add-on which increases network accuracy by empowering each artificial neuron with artificial dendrites.
For an example on [Relational Deep Learning](https://arxiv.org/abs/2312.04615) with the [RelBench datasets](https://relbench.stanford.edu/), see [`rdl.py`](./rdl.py).
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).
- [Kumo.ai x NVIDIA GNN+LLM Webinar](https://www.youtube.com/watch?v=uRIA8e7Y_vs)
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