Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk-sensitive domains. Existing defensive methods neither guarantee performance facing new data/tasks or adversarial attacks nor provide insights to understand GNN robustness from an architectural perspective. Neural Architecture Search (NAS) has the potential to solve this problem by automating GNN architecture designs. Nevertheless, current graph NAS approaches lack robust design and are vulnerable to adversarial attacks. To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA). Specifically, we design a robust search space for the message-passing mechanism by adding graph structure mask operations into the search space, which comprises various defensive operation candidates and allows us to search for defensive GNNs. Furthermore, we define a robustness metric to guide the search procedure, which helps to filter robust architectures. In this way, G-RNA helps understand GNN robustness from an architectural perspective and effectively searches for optimal adversarial robust GNNs. Extensive experimental results on benchmark datasets show that G-RNA significantly outperforms manually designed robust GNNs and vanilla graph NAS baselines by 12.1% to 23.4% under adversarial attacks.

翻译：图神经网络（GNNs）在建模关系数据方面取得了巨大成功，然而，它们容易受到对抗性攻击，这给将GNN应用于风险敏感领域带来了巨大威胁。现有的防御方法既无法保证在面对新数据/任务或对抗攻击时的性能，也无法从架构角度提供理解GNN鲁棒性的见解。神经架构搜索（NAS）通过自动化GNN架构设计有潜力解决这一问题。然而，当前的图NAS方法缺乏鲁棒性设计，且容易受到对抗攻击。为解决这些挑战，我们提出了一种新颖的鲁棒神经架构搜索框架（G-RNA）。具体地，我们通过在图搜索空间中添加结构掩码操作，为消息传递机制设计了一个鲁棒搜索空间，该空间包含多种防御操作候选，允许搜索防御性GNN。此外，我们定义了一个鲁棒性度量来指导搜索过程，这有助于筛选鲁棒架构。通过这种方式，G-RNA有助于从架构角度理解GNN鲁棒性，并有效搜索最优的对抗鲁棒GNN。在基准数据集上的大量实验结果表明，在对抗攻击下，G-RNA的性能比手动设计的鲁棒GNN和原始图NAS基线显著提升12.1%至23.4%。