Awesome
GraphAT
Code for the GraphAT, GraphVAT, and GCN-VAT in our paper "Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure", [paper].
Environment
Python 3.6.1 :: Continuum Analytics, Inc.
tensorflow 1.8.0
numpy 1.18.1
Reproduction
Once configured the required environment, the prediction performance reported in our paper can be reproduced by running the following commands (Table 4).
GraphAT
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
GraphVAT
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
GCN-VAT
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
Cite
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}
}