Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanisms of target HGNNs, overlooking the common vulnerability caused by the significant differences in hyperedge pivotality along aggregation paths in most HGNNs, thereby limiting the transferability and effectiveness of attacks. In this paper, we present a novel framework, i.e., Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges (TH-Attack), to address these limitations. Specifically, we design a hyperedge recognizer via pivotality assessment to obtain pivotal hyperedges within the aggregation paths of HGNNs. Furthermore, we introduce a feature inverter based on pivotal hyperedges, which generates malicious nodes by maximizing the semantic divergence between the generated features and the pivotal hyperedges features. Lastly, by injecting these malicious nodes into the pivotal hyperedges, TH-Attack improves the transferability and effectiveness of attacks. Extensive experiments are conducted on six authentic datasets to validate the effectiveness of TH-Attack and the corresponding superiority to state-of-the-art methods.

翻译：近期研究表明，超图神经网络（HGNNs）易受对抗性攻击的影响。然而，现有方法依赖于目标HGNNs的具体信息传递机制，忽视了大多数HGNNs中沿聚合路径的超边关键性差异显著所导致的共同脆弱性，从而限制了攻击的可迁移性和有效性。本文提出了一种新颖的框架——通过向关键超边注入节点的可迁移超图攻击（TH-Attack），以解决这些局限性。具体而言，我们设计了一种基于关键性评估的超边识别器，以获取HGNNs聚合路径中的关键超边。此外，我们引入了一种基于关键超边的特征反转器，通过最大化生成特征与关键超边特征之间的语义差异来生成恶意节点。最后，通过将这些恶意节点注入关键超边，TH-Attack提升了攻击的可迁移性和有效性。我们在六个真实数据集上进行了大量实验，验证了TH-Attack的有效性及其相较于现有先进方法的优越性。