Feb. 27, 2023, 2:10 a.m. | Chao Hu, Ruishi Yu, Binqi Zeng, Yu Zhan, Ying Fu, Quan Zhang, Rongkai Liu, Heyuan Shi

cs.CR updates on arXiv.org arxiv.org

Hypergraph neural networks (HGNN) have shown superior performance in various
deep learning tasks, leveraging the high-order representation ability to
formulate complex correlations among data by connecting two or more nodes
through hyperedge modeling. Despite the well-studied adversarial attacks on
Graph Neural Networks (GNN), there is few study on adversarial attacks against
HGNN, which leads to a threat to the safety of HGNN applications. In this
paper, we introduce HyperAttack, the first white-box adversarial attack
framework against hypergraph neural networks. HyperAttack …

adversarial adversarial attacks applications attack attacks box data deep learning high modeling networks neural networks nodes order performance representation safety study threat

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