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

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Deep Learning MVGCN
Overview

MVGCN

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.

Tutorial

  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 finetuning the hyperparameters by running the script command_optimal (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

Requirements

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

Contact

Please feel free to contact us if you need any help: [email protected]

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