Py_RAGCN/ReadMe.md

RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data

Here is the code for node classification in graphs with imbalanced classes written in Pytorch. Ghorbani et.al. “RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data” [1]

Usage

The main file is “main_medical.py”.

Input Data

For running the code, you need to change the data load function named “load_data_medical”. adjacency matrices, features, labels, training, validation, and test indices should be returned in this function. More description about each variable is as follows:

• adj: is a dictionary with the keys ’D’ and ‘W’. adj[’D’] contains the normalize adjacency matrix (with self-loop) between all nodes and is used for the discriminator. adj[‘W’] contains a list of normalized adjacency matrices (with self-loop). k-th element is the adjacency matrix between training samples with label k.
• Features: is a tensor that includes the features of all nodes (N by F).
• labels: is a list of labels for all nodes (with length N)
• idx_train, idx_val, idx_test: are lists of indexes for training, validation, and test samples respectively.

Parameters

Here is a list of parameters that should be passed to the main function or set in the code:

• epochs: number of epochs for training the whole network (default: 1000)
• epoch_D: number of epochs for training discriminator in each iteration (default: 1)
• epoch_W: number of epochs for training weighting networks in each iteration (default: 1)
• lr_D: learning for the discriminator (default: 0.01)
• lr_W: common learning rate for all weighting networks (default: 0.01)
• dropout_D: dropout for discriminator (default: 0.5)
• dropout_W: common dropout for all weighting networks (default: 0.5)
• gamma: a float number that shows the coefficient of entropy term in the loss function (default: 1)
• no-cuda: a boolean that can be set to True if using the GPU is not necessary
• structure_D: a list of hidden neurons in each layer of the discriminator. This variable should be set in the code (default: [2] which is a network with one hidden layer with two neurons in it)
• structure_W: a list of hidden neurons in each layer of all the weighting networks. This variable should be set in the code (default: [4])
• drop_epochs: to select the best model, we use the performance of the network on the validation set based on the macro F1 score. To choose the best performance and avoid the network when it is not stabilized yet, we drop a number of epochs at the start of the iterations (default: 500).

Metrics

Accuracy and macro F1 are calculated in the code. Binary F1 and ROAUC are calculated for binary classification tasks.

Note

Thanks to Thomas Kipf. The code is written based on the “Graph Convolutional Networks in PyTorch” [2].

Bug Report

If you find a bug, please send email to [email protected] including if necessary the input file and the parameters that caused the bug. You can also send me any comment or suggestion about the program.

References

[1] Ghorbani, Mahsa, et al. “RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data.” Medical Image Analysis 75 (2022): 102272.

[2] Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016

Cite

Please cite our paper if you use this code in your own work:

@article{ghorbani2022ra,
  title={Ra-gcn: Graph convolutional network for disease prediction problems with imbalanced data},
  author={Ghorbani, Mahsa and Kazi, Anees and Baghshah, Mahdieh Soleymani and Rabiee, Hamid R and Navab, Nassir},
  journal={Medical Image Analysis},
  volume={75},
  pages={102272},
  year={2022},
  publisher={Elsevier}
}