Graph Adversarial Immunization for Certifiable Robustness. (arXiv:2302.08051v1 [cs.LG])

Despite achieving great success, graph neural networks (GNNs) are vulnerable
to adversarial attacks. Existing defenses focus on developing adversarial
training or robust GNNs. However, little research attention is paid to the
potential and practice of immunization on graphs. In this paper, we propose and
formulate graph adversarial immunization, i.e., vaccinating part of graph
structure to improve certifiable robustness of graph against any admissible
adversarial attack. We first propose edge-level immunization to vaccinate node
pairs. Despite the primary success, such edge-level …

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