dgl/examples/pytorch/evolveGCN

Implement EvolveGCN with DGL

paper link: EvolveGCN
official code: IBM/EvolveGCN
another implement: pyG_temporal

Dependency:

• dgl
• pandas
• numpy

Run

• donwload Elliptic dataset from kaggle
• unzip the dataset into a raw directory, such as /home/Elliptic/elliptic_bitcoin_dataset/
• make a new dir to save processed data, such as /home/Elliptic/processed/
• run train.py by:

python train.py --raw-dir /home/Elliptic/elliptic_bitcoin_dataset/ --processed-dir /home/Elliptic/processed/

Result

Using EvolveGCN-O can match the results of Fig.3 and Fig.4 in the paper. (May need to run several times to get the average)

Attention:

• Currently only the Elliptic dataset is used.
• EvolveGCN-H is not solid in Elliptic dataset, the official code is the same.

Official code result when use EvolveGCN-H:

1. set seed to 1234, finally result is :

TEST epoch 189: TEST measures for class 1 - precision 0.3875 - recall 0.5714 - f1 0.4618

2. not set seed manually, run the same code three times:

TEST epoch 168: TEST measures for class 1 - precision 0.3189 - recall 0.0680 - f1 0.1121
TEST epoch 270: TEST measures for class 1 - precision 0.3517 - recall 0.3018 - f1 0.3249
TEST epoch 455: TEST measures for class 1 - precision 0.2271 - recall 0.2995 - f1 0.2583