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1 Ergebnisse
1
GUARD: Graph Universal Adversarial Defense:
, In:
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
,
Li, Jintang
;
Liao, Jie
;
Wu, Ruofan
... - p. 1198-1207 , 2023
Link:
https://dl.acm.org/doi/10.1145/3583780.3614903
RT T1 <cTitle>Proceedings of the 32nd ACM International Conference on Information and Knowledge Management</cTitle>: T1 <Title>GUARD: Graph Universal Adversarial Defense</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3614903&Exemplar=1&LAN=DE A1 Li, Jintang A1 Liao, Jie A1 Wu, Ruofan A1 Chen, Liang A1 Zheng, Zibin A1 Dan, Jiawang A1 Meng, Changhua A1 Wang, Weiqiang PB ACM YR 2023 K1 graph adversarial attack K1 graph adversarial defense K1 graph neural networks K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Learning latent representations K1 Networks K1 Network properties K1 Network reliability K1 Security and privacy K1 Software and application security SP 1198 OP 1207 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3583780.3614903 DO https://dl.acm.org/doi/10.1145/3583780.3614903 SF ELIB - SuUB Bremen
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