Code for TOIS 2021 paper "Direction-Aware User Recommendation Based on Asymmetric Network Embedding", the final version of the paper will also be released soon:)
We have provided nine directed network dataset including all the datasets used in this paper and some other small datasets for fast evaluation.
- Citeseer(labeled)
- Cora(labeled)
- Cocit(labeled)
- Epinions
- LastFM
- Pubmed(labeled)
- Slashdot
- Wiki
We have provided both the Tensorflow and Pytorch implementation of DNE. The requirements of the running environment is listed in requirements.txt. You can create the environment with anaconda:
conda install --yes --file requirements.txt
or virtualenv:
pip install -r requirements.txt
Then, the code can be run by:
python main_tf.py (for Tensorflow users)
or
python main_torch.py (for Pytorch users)
For the parameters used in the code, see the help of the argparse.
Please consider citing DNE in your publications if it helps your research.
@article{sheng2021direction,
title={Direction-Aware User Recommendation Based on Asymmetric Network Embedding},
author={Sheng Zhou, Xin Wang, Martin Ester, Bolang Li, Chen Ye, Zhen Zhang, Can Wang, Jiajun Bu},
journal={ACM Transactions on Information Systems},
year={2021}
}