# Momentum Gradient-based Untargeted Attack on Hypergraph Neural Networks

arXiv: https://arxiv.org/abs/2310.15656 · HTML: https://mlsanity.com/paper/2310.15656/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 63 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 30 of 35)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [Graph Structure Learning for Robust Graph Neural Networks](https://mlsanity.com/paper/2005.10203/) (2020)
- [Adversarial Attack on Graph Structured Data](https://mlsanity.com/paper/1806.02371/) (2018)
- [Adversarial Attacks on Graph Neural Networks via Meta Learning](https://mlsanity.com/paper/1902.08412/) (2019)
- [BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks](https://mlsanity.com/paper/2204.07054/) (2022)
- [Fast Gradient Attack on Network Embedding](https://mlsanity.com/paper/1809.02797/) (2018)
- [Understanding and Improving Graph Injection Attack by Promoting Unnoticeability](https://mlsanity.com/paper/2202.08057/) (2022)
- [Scalable Attack on Graph Data by Injecting Vicious Nodes](https://mlsanity.com/paper/2004.13825/) (2020)
- [Transferable Graph Backdoor Attack](https://mlsanity.com/paper/2207.00425/) (2022)
- [Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2211.10782/) (2022)
- [Graph Structural Attack by Perturbing Spectral Distance](https://mlsanity.com/paper/2111.00684/) (2021)
- [Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias](https://mlsanity.com/paper/2304.00010/) (2023)
- [Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function](https://mlsanity.com/paper/2009.00203/) (2020)
- [Projective Ranking-based GNN Evasion Attacks](https://mlsanity.com/paper/2202.12993/) (2022)
- [Learning Hypergraph-regularized Attribute Predictors](https://mlsanity.com/paper/1503.05782/) (2015)
- [Message Passing Neural Networks for Hypergraphs](https://mlsanity.com/paper/2203.16995/) (2022)
- [A Targeted Universal Attack on Graph Convolutional Network](https://mlsanity.com/paper/2011.14365/) (2020)
- [Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks](https://mlsanity.com/paper/2209.05957/) (2022)
- [Adversarial Camouflage for Node Injection Attack on Graphs](https://mlsanity.com/paper/2208.01819/) (2022)
- [MGA: Momentum Gradient Attack on Network](https://mlsanity.com/paper/2002.11320/) (2020)
- [GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections](https://mlsanity.com/paper/2210.12598/) (2022)
- [Directed hypergraph neural network](https://mlsanity.com/paper/2008.03626/) (2020)
- [Visual Analytics for Temporal Hypergraph Model Exploration](https://mlsanity.com/paper/2008.07299/) (2020)
- [Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs](https://mlsanity.com/paper/2009.00163/) (2020)
- [Node Injection for Class-specific Network Poisoning](https://mlsanity.com/paper/2301.12277/) (2023)
- [Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective](https://mlsanity.com/paper/2208.09979/) (2022)

## Built on by (most cited first, 1 of 1)

- [Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges](https://mlsanity.com/paper/2412.18365/) (2024)

## Abstract (arXiv)

Hypergraph Neural Networks (HGNNs) have been successfully applied in various hypergraph-related tasks due to their excellent higher-order representation capabilities. Recent works have shown that deep learning models are vulnerable to adversarial attacks. Most studies on graph adversarial attacks have focused on Graph Neural Networks (GNNs), and the study of adversarial attacks on HGNNs remains largely unexplored. In this paper, we try to reduce this gap. We design a new HGNNs attack model for the untargeted attack, namely MGHGA, which focuses on modifying node features. We consider the process of HGNNs training and use a surrogate model to implement the attack before hypergraph modeling. Specifically, MGHGA consists of two parts: feature selection and feature modification. We use a momentum gradient mechanism to choose the attack node features in the feature selection module. In the feature modification module, we use two feature generation approaches (direct modification and sign gradient) to enable MGHGA to be employed on discrete and continuous datasets. We conduct extensive experiments on five benchmark datasets to validate the attack performance of MGHGA in the node and the visual object classification tasks. The results show that MGHGA improves performance by an average of 2% compared to the than the baselines.