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Proceedings of the AAAI Conference on Artificial Intelligence

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Transferable Hypergraph Attack via Injecting Nodes into P...
Meixia He, P · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Meixia He School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University
  • Peican Zhu School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University
  • Le Cheng School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University School of Computer Science, Northwestern Polytechnical University
  • Yangming Guo School of Cybersecurity, Northwestern Polytechnical University
  • Manman Yuan School of Computer Science, Inner Mongolia University
  • Keke Tang Cyberspace Institute of Advanced Technology, Guangzhou University Huangpu Research School of Guangzhou University

DOI:

https://doi.org/10.1609/aaai.v40i1.36999

Abstract

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.

How to Cite

He, M., Zhu, P., Cheng, L., Guo, Y., Yuan, M., & Tang, K. (2026). Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 372–380. https://doi.org/10.1609/aaai.v40i1.36999

Issue

Section

AAAI Technical Track on Application Domains I

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