Implementation of paper "Transferring Robustness for Graph Neural Network Against Poisoning Attacks"

by Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang
Published at WSDM 2020, Houston, Texas, USA

Please cite our paper if the model or the paper help:

@inproceedings{tang2020transferring,
	title = {Transferring Robustness for Graph Neural Network Against Poisoning Attacks},
	author={Tang, Xianfeng and Li, Yandong and Sun, Yiwei and Yao, Huaxiu and Mitra, Prasenjit and Wang, Suhang},
	booktitle={ACM Internatioal Conference on Web Search and Data Mining (WSDM)},
	year = {2020}
}

• Python 3.7 or newer
• numpy
• tensorflow
• scipy

Please download data.zip and extract all contents to data/.

Please run with python main.py.

Please contact tangxianfeng at outlook.com for any questions.

We acquire the processed graph from https://github.com/tkipf/gcn/tree/master/gcn/data and put them in data/gcn_data (must be unzip from data.zip to find it). The original datasets can be found here: http://linqs.cs.umd.edu/projects/projects/lbc/.

The original Reddit graph can be found here: http://snap.stanford.edu/graphsage/.

We use Yelp Dataset to compile these two datasets.

The design and implenmentation of meta-learning part is inspired by MAML-TensorFlow and maml.

The design of neural networks is inspired by gcn and metattack.

We adopt nettack and metattack.