Code for "Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification".
- main.py: the core of our model, including the structure and the process of training.
- calculator.py: the code about calculating Poincare embedding and class-aware Ricci curvature weights
- dataloader.py: providing data loading and processing.
- models/: including the backbones we used in the main model.
Our experimental environments are listed in environments.yaml, you can create a virtual environment with conda and run the following order.
conda env create -f environments.yaml
Enter the virtual environment and run the requirements.txt.
pip install -r requirements.txt
All the datasets are provided by pytorch_geometric.
Run the following order to train our model.
python main.py
@inproceedings{fu2023hyperimba,
title={Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification},
author={Fu Xingcheng, Wei Yuecen, Sun Qingyun, Yuan Haonan, Wu Jia, Peng Hao and Li Jianxin},
booktitle={Proceedings of the 2022 World Wide Web Conference},
year={2022}
}