X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning

This is the PyTorch implementation of the paper:

X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning, CIKM'2022
Baoyu Jing, Shengyu Feng, Yuejia Xiang, Xi Chen, Yu Chen and Hanghang Tong

Requirements

• Python=3.10
• numpy=1.23.5
• scipy=1.9.3
• scikit-learn=0.22.0
• tqdm=4.64.1
• torch=1.13.0

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

Data

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.

Run

1. Download the pre-processed data from here and put it to the folder data.
2. Specify the arguments in the xgoal_{datasetname}.py.
3. Run the code by python xgoal_{datasetname}.py.
4. goal_example.py is an example file for the GOAL model.

Citation

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

X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning, CIKM'2022
Baoyu Jing, Shengyu Feng, Yuejia Xiang, Xi Chen, Yu Chen and Hanghang Tong

@article{jing2021x,
  title={X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning},
  author={Jing, Baoyu and Feng, Shengyu and Xiang, Yuejia and Chen, Xi and Chen, Yu and Tong, Hanghang},
  journal={arXiv preprint arXiv:2109.03560},
  year={2021}
}