MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

Developer: Fu Haitao from BBDM lab, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

1. Split data for cross validation and indenpendent test experiment via the script split_data.py: python split_data.py fold_number DATANAME seed_indent seed_cross

2. To perform cross validation for finding the optimal hyperparameters by running the script command_optimal.py (if you don't want to finetune the hyperparameters, just skip this step):

   python command_optimal.py --dataName DATANAME --exp_name mid_dim/num_layer/alp_beta --seed_cross seed_cross --seed_indent seed_indent

3. To get the experiment results by running the script command_optimal.py:

   python command_optimal.py --dataName DATANAME --exp_name optimal_cross --seed_cross seed_cross --seed_indent seed_indent

   python command_optimal.py --dataName DATANAME --exp_name optimal_indent --seed_cross seed_cross --seed_indent seed_indent

numpy 1.18.0

pandas 1.1.0

scipy 1.4.1

scikit-learn 0.22

tensorflow 1.15.0

pytorch 1.6.0

python 3.7.1

• Please feel free to contact us if you need any help: fuhaitao@webmail.hzau.edu.cn OR fuhaitao95@qq.com
• Attention: Only real name emails will be replied. Please provide as much detail as possible about the problem you are experiencing.
• 注意：只回复实名电子邮件。请尽可能详细地描述您遇到的问题，可以附上截图等。