This is a PYG implementation of I2BGNN, as described in the following:

Identity inference on blockchain using graph neural network

For hardware configuration, the experiments are conducted at Ubuntu 18.04.5 LTS with the Intel(R) Xeon(R) Gold 5218R CPU @ 2.10GHz, and NVIDIA Tesla V100S GPU (with 40GB memory each). For software configuration, all model are implemented in

• Python 3.7
• Pytorch-Geometric 2.0.3
• Pytorch 1.8.0
• Scikit-learn 0.24.1
• CUDA 10.2

Download data from this link and place it under the 'data/eth/' path.

Execute the following bash commands in the same directory where the code resides:

$ python main.py -l p --hop 2 -ess Volume -layer 2 --pooling max --hidden_dim 128 --batch_size 32 --lr 0.001 --dropout 0.2 -undir 1 -which_ew Volume

More parameter settings can be found in 'utils/parameters.py'.

If you find this work useful, please cite the following:

@inproceedings{shen2021identity,
  title={Identity inference on blockchain using graph neural network},
  author={Shen, Jie and Zhou, Jiajun and Xie, Yunyi and Yu, Shanqing and Xuan, Qi},
  booktitle={International Conference on Blockchain and Trustworthy Systems},
  pages={3--17},
  year={2021},
  organization={Springer}
}