This is the PyTorch implementation of the paper:

Baoyu Jing, Chanyoung Park and Hanghang Tong, HDMI: High-order Deep Multiplex Infomax, WWW'2021

• Python 3.6
• numpy>=1.19.5
• scipy>=1.5.4
• scikit-learn>=0.24.1
• tqdm>=4.59.0
• torch>=1.6.0
• torchvision>=0.7.0

Packages can be installed via: pip install -r requirements.txt. For PyTorch, please install the version compatible with your machine.

The pre-processed data can be downloaded from here. Please put the pre-processed data under the folder data. Each pre-processed dataset is a dictionary containing the following keys:

• train_idx, val_idx and test_idx are indices for training, validation and testing; label corresponds to the labels of the nodes;
• the layer names of the dataset: e.g., MAM and MDM for the imdb dataset.

1. Download the pre-processed data from here and put it to the folder data.
2. Specify the arguments in the main.py.
3. Run the code by python main.py.

Please cite the following paper, if you find the repository or the paper useful.

Baoyu Jing, Chanyoung Park and Hanghang Tong, HDMI: High-order Deep Multiplex Infomax, WWW'2021

@article{jing2021hdmi,
  title={HDMI: High-order Deep Multiplex Infomax},
  author={Jing, Baoyu and Park, Chanyoung and Tong, Hanghang},
  journal={arXiv preprint arXiv:2102.07810},
  year={2021}
}