  Graph neural networks (GNNs) have been increasingly deployed in various
applications that involve learning on non-Euclidean data. However, recent
studies show that GNNs are vulnerable to graph adversarial attacks. Although
there are several defense methods to improve GNN robustness by eliminating
adversarial components, they may also impair the underlying clean graph
structure that contributes to GNN training. In addition, few of those defense
models can scale to large graphs due to their high computational complexity and
memory usage. In this paper, we propose GARNET, a scalable spectral method to
boost the adversarial robustness of GNN models. GARNET first leverages weighted
spectral embedding to construct a base graph, which is not only resistant to
adversarial attacks but also contains critical (clean) graph structure for GNN
training. Next, GARNET further refines the base graph by pruning additional
uncritical edges based on probabilistic graphical model. GARNET has been
evaluated on various datasets, including a large graph with millions of nodes.
Our extensive experiment results show that GARNET achieves adversarial accuracy
improvement and runtime speedup over state-of-the-art GNN (defense) models by
up to 13.27% and 14.7x, respectively.
