wehub-resource-sync
56 lines
1.5 KiB
Markdown
56 lines
1.5 KiB
Markdown
# DGL Implementation of the SEAL Paper
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This DGL example implements the link prediction model proposed in the paper
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[Link Prediction Based on Graph Neural Networks](https://arxiv.org/pdf/1802.09691.pdf)
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and [REVISITING GRAPH NEURAL NETWORKS FOR LINK PREDICTION](https://arxiv.org/pdf/2010.16103.pdf)
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The author's codes of implementation is in [SEAL](https://github.com/muhanzhang/SEAL) (pytorch)
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and [SEAL_ogb](https://github.com/facebookresearch/SEAL_OGB) (torch_geometric)
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Example implementor
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----------------------
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This example was implemented by [Smile](https://github.com/Smilexuhc) during his intern work at the AWS Shanghai AI Lab.
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The graph dataset used in this example
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---------------------------------------
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ogbl-collab
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- NumNodes: 235868
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- NumEdges: 2358104
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- NumNodeFeats: 128
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- NumEdgeWeights: 1
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- NumValidEdges: 160084
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- NumTestEdges: 146329
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Dependencies
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--------------------------------
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- python 3.6+
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- Pytorch 1.5.0+
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- dgl 0.6.0 +
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- ogb
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- pandas
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- tqdm
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- scipy
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How to run example files
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--------------------------------
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In the seal_dgl folder
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run on cpu:
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```shell script
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python main.py --gpu_id=-1 --subsample_ratio=0.1
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```
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run on gpu:
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```shell script
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python main.py --gpu_id=0 --subsample_ratio=0.1
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```
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Performance
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-------------------------
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experiment on `ogbl-collab`
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| method | valid-hits@50 | test-hits@50 |
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| ------ | ------------- | ------------ |
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| paper | 63.89(0.49) | 53.71(0.47) |
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| ours | 63.56(0.71) | 53.61(0.78) |
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Note: We only perform 5 trails in the experiment. |