Introduction

The implementation of the paper "Simple and Efficient Partial Graph Adversarial Attack: A New Perspective", under the setting of global attack, treats different nodes differently to perform more efficient adversarial attacks.

Main Structure

• models: implementation of GNN models
• victims: experiments for training
  • configs: configurations of models
  • models: trained models
• attackers: implementation of attack methods
• attack: experiments for attacking
  • configs: hyperparameter of attackers
  • perturbed_adjs: adversarial adj generated

Running Step

1. training models

> cd victims
> python train.py --model=gcn --dataset=cora

2. performing attacks

> cd attack
> python gen_attack.py 

PGA

1. training models

> cd victims
> python train.py

2. performing attack

> cd attack
> python gen_attack.py --attack=pga --dataset=cora

Evaluation (evasion attack)

> python evasion_attack.py --victim=robust --dataset=cora
> python evasion_attack.py --victim=normal --dataset=cora

Evaluation (poisoning attack)

> python poison_attack.py --victim=gcn --dataset=cora
> python poison_attack.py --victim=gat --dataset=cora

requirements

• deeprobust
• torch_geometry
• torch_sparse
• torch_scatter