HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks
This addresses a safety threat for HGNN applications by providing the first efficient adversarial attack framework, though it is incremental as it adapts existing gradient-based methods to a new domain.
The paper tackles the lack of adversarial attack studies on hypergraph neural networks (HGNN) by introducing HyperAttack, a white-box structure attack framework that perturbs hyperedge links using gradients and integrated gradients, achieving a 10-20X improvement in time efficiency and increasing attack success rates by 1.3%-3.7% compared to state-of-the-art GNN methods.
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.