HyperMGA: momentum gradient attack of hypergraph neural networks

Hypergraph neural networks have demonstrated outstanding performance in various fields. However, there is still a relative lack of research on the security aspects of hypergraph neural networks, particularly in terms of adversarial attack methods, when compared to graph neural networks. The existence of this research gap poses a potential threat to the security of deep learning applications that use hypergraph neural networks. To enhance the reliability and security of hypergraph neural networks, we propose HyperMGA, an adversarial attack method specifically designed for hypergraphs. HyperMGA utilizes gradient momentum to effectively update the adversarial network and produce robust attack results. In comparative evaluations, HyperMGA outperforms state-of-the-art methods including integrated gradient algorithms, hypergraph attacks, fast gradient attacks, and stochastic attacks by a factor of 2 to 2.8 in terms of attack success rate. Additionally, HyperMGA’s runtime is comparable to that of FGA, which is only 36% of that of IGA. The objective of this research is to enhance the security of hypergraph neural networks against attack methods in various applications. This will be achieved by bridging the existing gap in knowledge.