  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.
