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:
this https URL

图神经网络（GNN）是处理图结构数据的主要方法，但存在对小的对抗性扰动脆弱性的问题。本文介绍了一种新的防御方法 NoisyGNNs，它通过在模型架构中引入噪声来提高 GNN 的鲁棒性。通过理论分析和实证评估，揭示了噪声注入和 GNN 鲁棒性增强之间的关联，并展示了 NoisyGNN 在节点分类任务上的卓越性能。这种方法是模型无关的，可与不同的 GNN 架构集成，与现有的防御技术结合使用可以进一步提高对抗性防御效果。