# Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges

arXiv: https://arxiv.org/abs/2412.18365 · HTML: https://mlsanity.com/paper/2412.18365/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 43 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 22 of 22)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [Adversarial Attack and Defense on Graph Data: A Survey](https://mlsanity.com/paper/1812.10528/) (2018)
- [Higher-order organization of complex networks](https://mlsanity.com/paper/1612.08447/) (2016)
- [Adversarial Attacks on Node Embeddings via Graph Poisoning](https://mlsanity.com/paper/1809.01093/) (2018)
- [Hiding Individuals and Communities in a Social Network](https://mlsanity.com/paper/1608.00375/) (2016)
- [Hyper-SAGNN: a self-attention based graph neural network for hypergraphs](https://mlsanity.com/paper/1911.02613/) (2019)
- [A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models](https://mlsanity.com/paper/1908.01297/) (2019)
- [TDGIA:Effective Injection Attacks on Graph Neural Networks](https://mlsanity.com/paper/2106.06663/) (2021)
- [Fast Gradient Attack on Network Embedding](https://mlsanity.com/paper/1809.02797/) (2018)
- [The physics of higher-order interactions in complex systems](https://mlsanity.com/paper/2110.06023/) (2021)
- [Scalable Attack on Graph Data by Injecting Vicious Nodes](https://mlsanity.com/paper/2004.13825/) (2020)
- [Single Node Injection Attack against Graph Neural Networks](https://mlsanity.com/paper/2108.13049/) (2021)
- [Improving Graph Neural Networks with Simple Architecture Design](https://mlsanity.com/paper/2105.07634/) (2021)
- [Adversarial Camouflage for Node Injection Attack on Graphs](https://mlsanity.com/paper/2208.01819/) (2022)
- [A General Black-box Adversarial Attack on Graph-based Fake News Detectors](https://mlsanity.com/paper/2404.15744/) (2024)
- [GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion](https://mlsanity.com/paper/2403.00014/) (2024)
- [HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks](https://mlsanity.com/paper/2302.12407/) (2023)
- [Characterizing cycle structure in complex networks](https://mlsanity.com/paper/2001.08541/) (2020)
- [Momentum Gradient-based Untargeted Attack on Hypergraph Neural Networks](https://mlsanity.com/paper/2310.15656/) (2023)

## Built on by (most cited first, 4 of 4)

- [KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection](https://mlsanity.com/paper/2507.09647/) (2025)
- [Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems](https://mlsanity.com/paper/2506.08401/) (2025)
- [Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field](https://mlsanity.com/paper/2412.19015/) (2024)
- [Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks](https://mlsanity.com/paper/2607.04091/) (2026)

## 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.