Awesome
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
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}
}
Requirements
- Python 3.7 or newer
numpytensorflowscipy
Before running
Please download data.zip and extract all contents to data/.
Run the code
Please run with python main.py.
Contact
Please contact tangxianfeng at outlook.com for any questions.
References
Dataset
Pubmed
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/.
Yelp small & large
We use Yelp Dataset to compile these two datasets.
Code & model design
Meta-learning
The design and implenmentation of meta-learning part is inspired by MAML-TensorFlow and maml.
Graph neural networks
The design of neural networks is inspired by gcn and metattack.