The Implemention of paper "Adversarial Attack on Community Detection by Hiding Individuals"[1]. It is accepted by The Web Conference 2020.
To train the CD-ATTACK model, please run the main.py as python main.py
To restore a trained model, the command is python main.py --test --trained_our_path [THE CHECKPOINT NAME]
The checkpoint name is formated as the string of the time point of starting the training process. eg.
python main.py --test --trained_our_path 200307133445 .The checkpoints will be recorded automatically for every training process. And the checkpoints files are placed in directory checkpoints/
The default dataset is dblp with fixed target users. To change the other dataset or modify other changeable parameters, please run python main.py -h to see the details.
The model is implemented based on python=3.6.7 and tensorflow =1.15. Other requirements of the enviorment is listed in requirements.txt.
The code is training on Nvidia-TitanX GPU with 12 Gb RAM. The CPU is i7-7800X and the memory is 64Gb. This is not the minimum required setting for this project. Other hardware setting may also feasible for this implemention.
[1] Li, Jia, et al. "Adversarial Attack on Community Detection by Hiding Individuals." In Proceedings of the ACM International World Wide Web Conference (WWW 2020).