  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 conducts a white-box
structure attack by perturbing hyperedge link status towards the target node
with the guidance of both gradients and integrated gradients. We evaluate
HyperAttack on the widely-used Cora and PubMed datasets and three hypergraph
neural networks with typical hypergraph modeling techniques. Compared to
state-of-the-art white-box structural attack methods for GNN, HyperAttack
achieves a 10-20X improvement in time efficiency while also increasing attack
success rates by 1.3%-3.7%. The results show that HyperAttack can achieve
efficient adversarial attacks that balance effectiveness and time costs.

Machine Learning;Artificial Intelligence;Cryptography and Security