Heterogeneous Graph Attention Networks

This is our Pytorch implementation for the paper:

Weijian Chen, Yulong Gu, Zhaochun Ren, Xiangnan He, Hongtao Xie, Tong Guo, Dawei Yin and Yongdong Zhang (2019). Semi-supervised User Profiling with Heterogeneous Graph Attention Networks. In IJCAI'19, Macao, China, August 10-16, 2019.

This work was done during my internship at JD Data Science Lab.

Citation

If you want to use our codes and dataset in your research, please cite:

@inproceedings{DBLP:conf/ijcai/ChenGRHXGYZ19,
  author    = {Weijian Chen and
               Yulong Gu and
               Zhaochun Ren and
               Xiangnan He and
               Hongtao Xie and
               Tong Guo and
               Dawei Yin and
               Yongdong Zhang},
  title     = {Semi-supervised User Profiling with Heterogeneous Graph Attention
               Networks},
  booktitle = {IJCAI},
  pages     = {2116--2122},
  year      = {2019}
}

Environment Requirement

The code has been tested running under Python 3.6.9. The required packages are as follows:

• pytorch == 1.0.1
• numpy == 1.17.2
• scikit-learn == 0.21.3

Example to Run the Codes

The instruction of commands has been clearly stated in the codes (see the parser function in train.py).

• HGAT, Gender Prediction

CUDA_VISIBLE_DEVICES=0 python train.py --pkl-dir 00 --data-dir data --model gat --hidden-units 16,16 --heads 8,8,1 --train-ratio 75 --valid-ratio 12.5 --instance-normalization --weight-decay 5e-4 --class-weight-balanced --patience 10 --epochs 100 --task gender --use-word-feature --lr 0.005 --dropout 0.6 --batch 64

• HGCN, Age Prediction

CUDA_VISIBLE_DEVICES=1 python train.py --pkl-dir 01 --data-dir data --model gcn --hidden-units 128,128 --train-ratio 75 --valid-ratio 12.5 --instance-normalization --weight-decay 5e-4 --class-weight-balanced --patience 10 --epochs 100 --task age --use-word-feature --lr 0.1 --dropout 0.2 --batch 32

Dataset

The dataset used in our paper has been provided by JD Data Science Lab, which can be downloaded here: https://github.com/guyulongcs/IJCAI2019_HGAT.

Related Links

• DeepInf: The main reference for our code implementation.
• RHGN: This work selects our work as the benchmark and provides another dataset.
• CatGCN: Our latest work involves user profiling.