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Designed for inductive learning, our framework generalizes to unseen nodes and applies broadly to other smoothing s","title":"AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing","url":"https://arxiv.org/abs/2503.22998","vendor":"arxiv_cs_ai"},"summary":"arXiv:2503.22998v3 Announce Type: replace-cross \nAbstract: Despite advancements in Graph Neural Networks (GNNs), adaptive attacks continue to challenge their robustness. Certified robustness via randomized smoothing offers provable guarantees but suffers from a severe accuracy-robustness trade-off, limiting its practical use. To bridge this gap, we introduce AuditVotes, the first framework that simultaneously achieves high clean accuracy and strong certified robustness. 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