dkhonker/args

Unofficial repository for "Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets"

Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets

《Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets》的非官方复现。 This is the unofficial repository for Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets which has been accepted at the NeurIPS 2023 GLFrontiers

Table of Contents

1. 安装/Installation
2. 使用/Usage
3. Baseline Results

Installation

（本人使用的是python 3.10) This project requires Python 3.8 and the following Python libraries installed:

• torch==1.13.1
• dgl==1.0.4 -cu116
• scikit-learn
• networkx
• scipy
• matplotlib
• pickle-mixin
• argparse
• numpy
• warnings

要安装这些依赖项，cd到项目目录并运行以下命令: To install these dependencies, navigate to your project's directory and run the following command:

pip install -r requirements.txt

Usage

You can test the performance of the trained adversarially robust GLTs by executing these commands on the terminal:

Evaluating the performance of ARGLTs for Cora and Citeseer Datasets attacked by PGD attack

python main_test.py --dataset cora --embedding-dim 1433 512 7 --attack_name pgd --ptb_rate 0.05
python main_test.py --dataset cora --embedding-dim 1433 512 7 --attack_name pgd --ptb_rate 0.1
python main_test.py --dataset cora --embedding-dim 1433 512 7 --attack_name pgd --ptb_rate 0.15
python main_test.py --dataset cora --embedding-dim 1433 512 7 --attack_name pgd --ptb_rate 0.2

python main_test.py --dataset citeseer --embedding-dim 3703 512 6 --attack_name pgd --ptb_rate 0.05
python main_test.py --dataset citeseer --embedding-dim 3703 512 6 --attack_name pgd --ptb_rate 0.1
python main_test.py --dataset citeseer --embedding-dim 3703 512 6 --attack_name pgd --ptb_rate 0.15
python main_test.py --dataset citeseer --embedding-dim 3703 512 6 --attack_name pgd --ptb_rate 0.2

Baseline Results

Here are the baseline accuracies for GCN on the Cora and Citeseer datasets under PGD attack for comparison:

GCN with PGD Attack on Cora Dataset

Perturbation Rate	Accuracy
0.05	78.81
0.1	78.46
0.15	77.93
0.2	77.99

GCN with PGD Attack on Citeseer Dataset

Perturbation Rate	Accuracy
0.05	73.28
0.1	73.96
0.15	74.62
0.2	73.52

You can also train the models by executing the following commands on the terminal:

Training ARGLT for Cora Dataset

 python main_train.py --dataset cora --embedding-dim 1433 512 7 --lr 0.008 --weight-decay 8e-5 --pruning_percent_wei 0.2 --pruning_percent_adj 0.05 --total_epoch 10 --s1 1e-2 --s2 1e-2 --init_soft_mask_type all_one --attack_name pgd --ptb_rate 0.05 --k 30 --alpha 1 --beta 0.01 --gamma 1 --alpha_fix_mask 1 --gamma_fix_mask 1

作者只提供了PGD的数据集。