Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges

• URL: http://arxiv.org/abs/2511.10698v1
• Date: Wed, 12 Nov 2025 12:41:51 GMT
• Title: Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges
• Authors: Meixia He, Peican Zhu, Le Cheng, Yangming Guo, Manman Yuan, Keke Tang,
• Abstract summary: Hypergraph neural networks (HGNNs) are susceptible to adversarial attacks.<n>We present Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges (TH-Attack)<n> TH-Attack improves the transferability and effectiveness of attacks.
• Score: 21.935993940930175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges [1.089691789591201]
  Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks.<n>We present a novel framework, i.e., Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges (IE-Attack)
  arXiv  Detail & Related papers  (2024-12-24T11:48:41Z)
• HGAttack: Transferable Heterogeneous Graph Adversarial Attack [63.35560741500611]
  Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce. This paper introduces HGAttack, the first dedicated gray box evasion attack method for heterogeneous graphs.
  arXiv  Detail & Related papers  (2024-01-18T12:47:13Z)
• Improving Adversarial Transferability by Stable Diffusion [36.97548018603747]
  adversarial examples introduce imperceptible perturbations to benign samples, deceiving predictions. Deep neural networks (DNNs) are susceptible to adversarial examples, which introduce imperceptible perturbations to benign samples, deceiving predictions. We introduce a novel attack method called Stable Diffusion Attack Method (SDAM), which incorporates samples generated by Stable Diffusion to augment input images.
  arXiv  Detail & Related papers  (2023-11-18T09:10:07Z)
• IDEA: Invariant Defense for Graph Adversarial Robustness [60.0126873387533]
  We propose an Invariant causal DEfense method against adversarial Attacks (IDEA) We derive node-based and structure-based invariance objectives from an information-theoretic perspective. Experiments demonstrate that IDEA attains state-of-the-art defense performance under all five attacks on all five datasets.
  arXiv  Detail & Related papers  (2023-05-25T07:16:00Z)
• Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature [36.78292952798531]
  We propose a Singular Value Decomposition (SVD)-based feature-level attack method. Our approach is inspired by the discovery that eigenvectors associated with the larger singular values from the middle layer features exhibit superior generalization and attention properties.
  arXiv  Detail & Related papers  (2023-05-02T12:27:44Z)
• Guidance Through Surrogate: Towards a Generic Diagnostic Attack [101.36906370355435]
  We develop a guided mechanism to avoid local minima during attack optimization, leading to a novel attack dubbed Guided Projected Gradient Attack (G-PGA) Our modified attack does not require random restarts, large number of attack iterations or search for an optimal step-size. More than an effective attack, G-PGA can be used as a diagnostic tool to reveal elusive robustness due to gradient masking in adversarial defenses.
  arXiv  Detail & Related papers  (2022-12-30T18:45:23Z)
• Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation [60.50994154879244]
  Recent studies show that Graph Neural Networks are vulnerable and easily fooled by small perturbations. In this work, we focus on the emerging but critical attack, namely, Graph Injection Attack. We propose a general defense framework CHAGNN against GIA through cooperative homophilous augmentation of graph data and model.
  arXiv  Detail & Related papers  (2022-11-15T11:44:31Z)
• What Does the Gradient Tell When Attacking the Graph Structure [44.44204591087092]
  We present a theoretical demonstration revealing that attackers tend to increase inter-class edges due to the message passing mechanism of GNNs. By connecting dissimilar nodes, attackers can more effectively corrupt node features, making such attacks more advantageous. We propose an innovative attack loss that balances attack effectiveness and imperceptibility, sacrificing some attack effectiveness to attain greater imperceptibility.
  arXiv  Detail & Related papers  (2022-08-26T15:45:20Z)
• BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection [20.666171188140503]
  Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs. These GAD tools expose a new attacking surface, ironically due to their unique advantage of being able to exploit the relations among data. In this paper, we exploit this vulnerability by designing a new type of targeted structural poisoning attacks to a representative regression-based GAD system OddBall.
  arXiv  Detail & Related papers  (2021-06-18T08:20:23Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)
• AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack [53.06334363586119]
  Recent studies have revealed the vulnerability of graph convolutional networks (GCNs) to edge-perturbing attacks. We first generalize the formulation of edge-perturbing attacks and strictly prove the vulnerability of GCNs to such attacks in node classification tasks. Following this, an anonymous graph convolutional network, named AN-GCN, is proposed to counter edge-perturbing attacks.
  arXiv  Detail & Related papers  (2020-05-06T08:15:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.