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GraphGallery is a gallery of state-of-the-arts graph neural networks for [TensorFlow 2.x](https://github.com/tensorflow/tensorflow) and [PyTorch](https://github.com/pytorch/pytorch). GraphGallery 0.4.x is a total re-write from previous versions, and some things have changed.
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# 👀 What's important
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Difference between GraphGallery and [pytorch geometric (PyG)](https://github.com/rusty1s/pytorch_geometric), [deep graph library (DGL)](https://github.com/dmlc/dgl), etc...
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+ PyG and DGL are just like **TensorFlow**, but GraphGallery is more like **Keras**
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Difference between GraphGallery and [Pytorch Geometric (PyG)](https://github.com/rusty1s/pytorch_geometric), [Deep Graph Library (DGL)](https://github.com/dmlc/dgl), etc...
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+ PyG and DGL are just like **TensorFlow** while GraphGallery is more like **Keras**
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+ GraphGallery is more friendly to use
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+ GraphGallery is more efficiient
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```bash
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pip install -U graphgallery
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```
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# :octocat: Implementations
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# 🤖 Implementations
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In detail, the following methods are currently implemented:
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## Semi-supervised models
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# verbose takes 0, 1, 2, 3, 4
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his = model.train(idx_train, idx_val, verbose=1, epochs=100)
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# test your model
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# verbose takes 0, 1
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# verbose takes 0, 1, 2
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loss, accuracy = model.test(idx_test, verbose=1)
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print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')
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```
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```
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# ❓ How to add your custom datasets
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# ❓ How to add your datasets
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This is motivated by [gnn-benchmark](https://github.com/shchur/gnn-benchmark/)
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