Code for the GraphAT, GraphVAT, and GCN-VAT in our paper "Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure", [paper].
Python 3.6.1 :: Continuum Analytics, Inc.
tensorflow 1.8.0
numpy 1.18.1
Once configured the required environment, the prediction performance reported in our paper can be reproduced by running the following commands (Table 4).
python gvat_citation.py --gat_loss=True --num_neighbors 2 --epsilon_graph 0.01 --beta 1.0 --dropout 0.0 --dataset cora --early_stopping 10
python gvat_citation.py --gat_loss=True --num_neighbors 2 --epsilon_graph 0.01 --beta 0.5 --dropout 0.0 --dataset citeseer --early_stopping 10
python gvat_citation.py --gat_loss=True --vat_loss=True --epsilon 1.0 --alpha 0.5 --xi 1e-05 --num_neighbors 2 --epsilon_graph 0.01 --beta 1.0 --dropout 0.0 --dataset cora --early_stopping 10
python gvat_citation.py --gat_loss=True --vat_loss=True --epsilon 1.0 --alpha 0.5 --xi 1e-06 --num_neighbors 2 --epsilon_graph 0.01 --beta 0.5 --dropout 0.0 --dataset citeseer --early_stopping 1
python vat_citation.py --epsilon 0.01 --alpha 1.0 --xi 0.001 --dropout 0.0 --dataset cora --early_stopping 10
python vat_citation.py --epsilon 0.05 --alpha 0.5 --xi 0.0001 --dropout 0.0 --dataset citeseer --early_stopping 10
If you use the code, please kindly cite the following paper:
@article{feng2019graph,
title={Graph adversarial training: Dynamically regularizing based on graph structure},
author={Feng, Fuli and He, Xiangnan and Tang, Jie and Chua, Tat-Seng},
journal={IEEE Transactions on Knowledge and Data Engineering},
year={2019},
publisher={IEEE}
}