@inproceedings{
hou2024robust,
title={Robust Graph Neural Networks via Unbiased Aggregation},
author={Zhichao Hou and Ruiqi Feng and Tyler Derr and Xiaorui Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dz6ex9Ee0Q}
}
The adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks despite the existence of numerous defenses.
In this work, we delve into the robustness analysis of representative robust GNNs and provide a unified robust estimation point of view to
understand their robustness and limitations.
Our novel analysis of estimation bias motivates the design of a 
robust and unbiased graph signal estimator. 
We then develop an efficient Quasi-Newton Iterative Reweighted Least Squares algorithm to solve the estimation problem, which is unfolded as robust unbiased aggregation layers in GNNs with theoretical guarantees.
Our comprehensive experiments confirm the strong robustness of our proposed model under various scenarios, and the ablation study provides a deep understanding of its advantages.
Robust Graph Neural Networks via Unbiased Aggregation
Zhichao Hou Ruiqi Feng Tyler Derr Xiaorui Liu