  Graph Neural Networks (GNNs) have emerged as the dominant approach for
machine learning on graph-structured data. However, concerns have arisen
regarding the vulnerability of GNNs to small adversarial perturbations.
Existing defense methods against such perturbations suffer from high time
complexity and can negatively impact the model's performance on clean graphs.
To address these challenges, this paper introduces NoisyGNNs, a novel defense
method that incorporates noise into the underlying model's architecture. We
establish a theoretical connection between noise injection and the enhancement
of GNN robustness, highlighting the effectiveness of our approach. We further
conduct extensive empirical evaluations on the node classification task to
validate our theoretical findings, focusing on two popular GNNs: the GCN and
GIN. The results demonstrate that NoisyGNN achieves superior or comparable
defense performance to existing methods while minimizing added time complexity.
The NoisyGNN approach is model-agnostic, allowing it to be integrated with
different GNN architectures. Successful combinations of our NoisyGNN approach
with existing defense techniques demonstrate even further improved adversarial
defense results. Our code is publicly available at:
https://github.com/Sennadir/NoisyGNN.
