wehub-resource-sync
36 lines
1.5 KiB
Markdown
36 lines
1.5 KiB
Markdown
# Implement EvolveGCN with DGL
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paper link: [EvolveGCN](https://arxiv.org/abs/1902.10191)
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official code: [IBM/EvolveGCN](https://github.com/IBM/EvolveGCN)
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another implement: [pyG_temporal](https://github.com/benedekrozemberczki/pytorch_geometric_temporal/blob/master/torch_geometric_temporal/nn/recurrent/evolvegcno.py)
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## Dependency:
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* dgl
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* pandas
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* numpy
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## Run
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* donwload Elliptic dataset from [kaggle](https://kaggle.com/ellipticco/elliptic-data-set)
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* unzip the dataset into a raw directory, such as /home/Elliptic/elliptic_bitcoin_dataset/
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* make a new dir to save processed data, such as /home/Elliptic/processed/
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* run train.py by:
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```bash
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python train.py --raw-dir /home/Elliptic/elliptic_bitcoin_dataset/ --processed-dir /home/Elliptic/processed/
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```
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## Result
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Using EvolveGCN-O can match the results of Fig.3 and Fig.4 in the paper.
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(May need to run several times to get the average)
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## Attention:
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* Currently only the Elliptic dataset is used.
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* EvolveGCN-H is not solid in Elliptic dataset, the official code is the same.
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Official code result when use EvolveGCN-H:
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1. set seed to 1234, finally result is :
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> TEST epoch 189: TEST measures for class 1 - precision 0.3875 - recall 0.5714 - f1 0.4618
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2. not set seed manually, run the same code three times:
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> TEST epoch 168: TEST measures for class 1 - precision 0.3189 - recall 0.0680 - f1 0.1121
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> TEST epoch 270: TEST measures for class 1 - precision 0.3517 - recall 0.3018 - f1 0.3249
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> TEST epoch 455: TEST measures for class 1 - precision 0.2271 - recall 0.2995 - f1 0.2583
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