This is the source code of our paper published in IEEE TKDE 2021 paper: Interpretable and Efficient Heterogeneous Graph Convolutional Network.
IEEE Xplore: https://ieeexplore.ieee.org/document/9508875
Pre-print version: https://arxiv.org/abs/2005.13183
The original dataset of IMDB is downloaded from HetRec 2011
The original datasets of ACM and DBLP are provided by the authors of [WWW 2019] [HAN] Heterogeneous Graph Attention Network
The preprocessed dataset are too large to be posted on GitHub. You can download preprocessed dataset and preprocessing script from the following links:
Baidu cloud drive: https://pan.baidu.com/s/1uTqp2H9a0bQImcjEE3HUjw, extraction code "dqbc".
OneDrive cloud drive: https://tinyurl.com/tkde-hgcn.
We also provide a concise version of source code in both of the two above cloud drives.
Please feel free to E-mail me if you cannot download them successfully.
If you make advantage of ie-HGCN in your research, please kindly cite our work as follows:
@article{yang2021interpretable,
title={Interpretable and efficient heterogeneous graph convolutional network},
author={Yang, Yaming and Guan, Ziyu and Li, Jianxin and Zhao, Wei and Cui, Jiangtao and Wang, Quan},
journal={IEEE Transactions on Knowledge and Data Engineering},
year={2021},
publisher={IEEE}
}