  Graph convolutional networks (GCNs) have been shown to be vulnerable to small
adversarial perturbations, which becomes a severe threat and largely limits
their applications in security-critical scenarios. To mitigate such a threat,
considerable research efforts have been devoted to increasing the robustness of
GCNs against adversarial attacks. However, current defense approaches are
typically designed to prevent GCNs from untargeted adversarial attacks and
focus on overall performance, making it challenging to protect important local
nodes from more powerful targeted adversarial attacks. Additionally, a
trade-off between robustness and performance is often made in existing
research. Such limitations highlight the need for developing an effective and
efficient approach that can defend local nodes against targeted attacks,
without compromising the overall performance of GCNs. In this work, we present
a simple yet effective method, named Graph Universal Adversarial Defense
(GUARD). Unlike previous works, GUARD protects each individual node from
attacks with a universal defensive patch, which is generated once and can be
applied to any node (node-agnostic) in a graph. GUARD is fast, straightforward
to implement without any change to network architecture nor any additional
parameters, and is broadly applicable to any GCNs. Extensive experiments on
four benchmark datasets demonstrate that GUARD significantly improves
robustness for several established GCNs against multiple adversarial attacks
and outperforms state-of-the-art defense methods by large margins.
