  Graph neural network (GNN) models play a pivotal role in numerous tasks
involving graph-related data analysis. Despite their efficacy, similar to other
deep learning models, GNNs are susceptible to adversarial attacks. Even minor
perturbations in graph data can induce substantial alterations in model
predictions. While existing research has explored various adversarial defense
techniques for GNNs, the challenge of defending against adversarial attacks on
real-world scale graph data remains largely unresolved. On one hand, methods
reliant on graph purification and preprocessing tend to excessively emphasize
local graph information, leading to sub-optimal defensive outcomes. On the
other hand, approaches rooted in graph structure learning entail significant
time overheads, rendering them impractical for large-scale graphs. In this
paper, we propose a new defense method named Talos, which enhances the global,
rather than local, homophily of graphs as a defense. Experiments show that the
proposed approach notably outperforms state-of-the-art defense approaches,
while imposing little computational overhead.
