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Domain-adaptive Transfer Learning with Graph Convolutional Networks

This repository contains the author's implementation in PyTorch for the paper "Coronary Heart Disease Prediction Method Fusing Domain-adaptive Transfer Learning with Graph Convolutional Networks (GCN)", Scientific Reports.

Dependencies

  • Python (>=3.6)
  • Torch (>=1.2.0)
  • numpy (>=1.16.4)
  • torch_scatter (>= 1.3.0)
  • torch_geometric (>= 1.3.0)

Datasets

The data folder includes different domain data. The datasets can be found in "/data/".

Implementation

Here we provide the implementation of DAMGCN, along with three domain datasets. The repository is organised as follows:

  • data/ contains the necessary dataset files for All-Cause, Heart Level and Mace occurs domain;
  • dual_gnn/ contains the implementation of the Global GCN and Local GCN;

Finally, DAMGCN_demo.py puts all of the above together and can be used to execute a full training run on the datasets.

Process

  • Place the datasets in data/
  • Change the dataset in DAMGCN_demo.py .
  • Training/Testing:
python DAMGCN_demo.py

Citation

@inproceedings{xue2023DAMGCN
author={Huizhong Lin, Kaizhi Chen, Yutao Xue .etc},
title={Coronary Heart Disease Prediction Method Fusing Domain-adaptive Transfer Learning with Graph Convolutional Networks (GCN)},
journal={Scientific Reports},
year={2023}
}

Thanks to the source code provided by the author of the "https://github.com/GRAND-Lab/UDAGCN" warehouse.