dmlc--dgl
VoVAllen
a00636a02b
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36 行
953 B
Markdown
36 行
953 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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- Tensorflow 2.1+
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- requests
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``bash
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pip install tensorflow 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_builtin.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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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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python3 train.py --dataset cora --gpu 0 --self-loop
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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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