dmlc--dgl
Minjie Wang
565f0c88fc
* refactor graph conv * docs & tests * fix lint * fix lint * fix lint * fix lint script * fix lint * Update * Style fix * Fix style * Fix style * Fix gpu case * Fix for gpu case * Hotfix edgesoftmax docs * Handle repeated features * Add docstring * Set default arguments * Remove dropout from nn.conv * Fix * add util fn for renaming * revert gcn_spmv.py * mx folder * fix wierd bug * fix mx * fix lint
37 行
994 B
Markdown
37 行
994 B
Markdown
Graph Convolutional Networks (GCN)
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============
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- Paper link: [https://arxiv.org/abs/1609.02907](https://arxiv.org/abs/1609.02907)
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- Author's code repo: [https://github.com/tkipf/gcn](https://github.com/tkipf/gcn). Note that the original code is
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implemented with Tensorflow for the paper.
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Dependencies
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------------
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- PyTorch 0.4.1+
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- requests
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``bash
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pip install torch requests
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``
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Codes
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-----
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The folder contains three implementations of GCN:
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- `gcn.py` uses DGL's predefined graph convolution module.
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- `gcn_mp.py` uses user-defined message and reduce functions.
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- `gcn_spmv.py` improves from `gcn_mp.py` by using DGL's builtin functions
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so SPMV optimization could be applied.
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Modify `train.py` to switch between different implementations.
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Results
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-------
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Run with following (available dataset: "cora", "citeseer", "pubmed")
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```bash
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python train.py --dataset cora --gpu 0
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```
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* cora: ~0.810 (0.79-0.83) (paper: 0.815)
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* citeseer: 0.707 (paper: 0.703)
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* pubmed: 0.792 (paper: 0.790)
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