Source code and datasets for KDD 2020 paper "Adaptive Graph Encoder for Attributed Graph Embedding"
Please make sure your environment includes:
python (tested on 3.7.4)
pytorch (tested on 1.2.1)
Then, run the command:
pip install -r requirements.txt
Run AGE on Cora dataset:
python train.py --dataset cora --gnnlayers 8 --upth_st 0.011 --lowth_st 0.1 --upth_ed 0.001 --lowth_ed 0.5
To reproduce the node clustering experiment results, please follow our hyper-parameter settings:
| Dataset | gnnlayers | upth_st | upth_ed | lowth_st | lowth_ed |
|---|---|---|---|---|---|
| Cora | 8 | 0.0110 | 0.0010 | 0.1 | 0.5 |
| Citeseer | 3 | 0.0015 | 0.0010 | 0.1 | 0.5 |
| Wiki | 1 | 0.0011 | 0.0010 | 0.1 | 0.5 |
| Pubmed | 35 | 0.0013 | 0.0010 | 0.7 | 0.8 |
For link prediction, please run link_pred.py. We did not tune hyper-parameters for link prediction, so you can tune all kinds of hyper-parameters to get better performance.
If you use the code, please cite our paper:
@inproceedings{cui2020adaptive,
title={Adaptive Graph Encoder for Attributed Graph Embedding},
author={Cui, Ganqu and Zhou, Jie and Yang, Cheng and Liu, Zhiyuan},
booktitle={Proceedings of SIGKDD 2020},
year={2020}
}