GUARD: Graph Universal Adversarial Defense. (arXiv:2204.09803v2 [cs.LG] UPDATED)

Graph convolutional networks (GCNs) have 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 approaches for defense are
typically designed for the whole graph and consider the global performance,
posing challenges in protecting important local nodes from stronger adversarial
targeted attacks. In this work, we present …

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