mlsanityData to 7 Oct 2026

Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges

arXiv 2412.18365 ↗First posted 24 Dec 2024cs.LG cs.AIcited by 4

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Abstract (arXiv)

Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by gradients, overlooking node spanning in the hypergraph and the group identity of hyperedges, thereby resulting in limited attack performance and detectable attacks. In this manuscript, we present a novel framework, i.e., Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges (IE-Attack), to tackle these challenges. Initially, utilizing the node spanning in the hypergraph, we propose the elite hyperedges sampler to identify hyperedges to be injected. Subsequently, a node generator utilizing Kernel Density Estimation (KDE) is proposed to generate the homogeneous node with the group identity of hyperedges. Finally, by injecting the homogeneous node into elite hyperedges, IE-Attack improves the attack performance and enhances the imperceptibility of attacks. Extensive experiments are conducted on five authentic datasets to validate the effectiveness of IE-Attack and the corresponding superiority to state-of-the-art methods.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 24 Dec 2024.

Builds on

22 of 43 references.

References · 22

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  4. 2019Hypergraph Convolution and Hypergraph Attentioncited by 226
  5. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 166
  6. 2016Higher-order organization of complex networkscited by 109
  7. 2018Adversarial Attacks on Node Embeddings via Graph Poisoningcited by 109
  8. 2016Hiding Individuals and Communities in a Social Networkcited by 77
  9. 2019Hyper-SAGNN: a self-attention based graph neural network for hypergraphscited by 69
  10. 2019A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Modelscited by 68
  11. 2021TDGIA:Effective Injection Attacks on Graph Neural Networkscited by 67
  12. 2018Fast Gradient Attack on Network Embeddingcited by 65
10 more
  1. 2021The physics of higher-order interactions in complex systemscited by 63
  2. 2020Scalable Attack on Graph Data by Injecting Vicious Nodescited by 50
  3. 2021Single Node Injection Attack against Graph Neural Networkscited by 45
  4. 2021Improving Graph Neural Networks with Simple Architecture Designcited by 41
  5. 2022Adversarial Camouflage for Node Injection Attack on Graphscited by 11
  6. 2024A General Black-box Adversarial Attack on Graph-based Fake News Detectorscited by 11
  7. 2024GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusioncited by 9
  8. 2023HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networkscited by 5
  9. 2020Characterizing cycle structure in complex networkscited by 3
  10. 2023Momentum Gradient-based Untargeted Attack on Hypergraph Neural Networkscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detectioncited by 4
  2. 2024Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Fieldcited by 0
  3. 2025Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systemscited by 0
  4. 2026Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networkscited by 0