# Recent Advances in Hypergraph Neural Networks

arXiv: https://arxiv.org/abs/2503.07959 · HTML: https://mlsanity.com/paper/2503.07959/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 131 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 30 of 44)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders](https://mlsanity.com/paper/1802.03480/) (2018)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://mlsanity.com/paper/2105.00956/) (2021)
- [You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks](https://mlsanity.com/paper/2106.13264/) (2021)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [HNHN: Hypergraph Networks with Hyperedge Neurons](https://mlsanity.com/paper/2006.12278/) (2020)
- [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://mlsanity.com/paper/2101.06448/) (2021)
- [Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation](https://mlsanity.com/paper/2012.06852/) (2020)
- [Be More with Less: Hypergraph Attention Networks for Inductive Text Classification](https://mlsanity.com/paper/2011.00387/) (2020)
- [Hyper-SAGNN: a self-attention based graph neural network for hypergraphs](https://mlsanity.com/paper/1911.02613/) (2019)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [Molecular Hypergraph Grammar with its Application to Molecular Optimization](https://mlsanity.com/paper/1809.02745/) (2018)
- [HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs](https://mlsanity.com/paper/2010.04558/) (2020)
- [Random Walks on Hypergraphs with Edge-Dependent Vertex Weights](https://mlsanity.com/paper/1905.08287/) (2019)
- [Self-supervised Hypergraph Representation Learning for Sociological Analysis](https://mlsanity.com/paper/2212.11440/) (2022)
- [A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide](https://mlsanity.com/paper/2404.01039/) (2024)
- [Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction](https://mlsanity.com/paper/2107.09928/) (2021)
- [Preventing Over-Smoothing for Hypergraph Neural Networks](https://mlsanity.com/paper/2203.17159/) (2022)
- [Session-based Recommendation with Hypergraph Attention Networks](https://mlsanity.com/paper/2112.14266/) (2021)
- [Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis](https://mlsanity.com/paper/2107.09953/) (2021)
- [Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders](https://mlsanity.com/paper/1804.10850/) (2018)

## Built on by (most cited first, 7 of 7)

- [SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition](https://mlsanity.com/paper/2505.15325/) (2025)
- [Implicit Hypergraph Neural Networks: A Stable Framework for Higher-Order Relational Learning with Provable Guarantees](https://mlsanity.com/paper/2508.09427/) (2025)
- [HyperPotter: Spell the Charm of High-Order Interactions in Audio Deepfake Detection](https://mlsanity.com/paper/2602.05670/) (2026)
- [High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach](https://mlsanity.com/paper/2603.02265/) (2026)
- [Hypergraph Neural Networks Accelerate MUS Enumeration](https://mlsanity.com/paper/2604.09001/) (2026)
- [ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction](https://mlsanity.com/paper/2607.18332/) (2026)
- [Optimizing Hypergraph-Based RAG: Toward Better Fact Extraction and Chunk Retrieval](https://mlsanity.com/paper/2607.20506/) (2026)

## Abstract (arXiv)

The growing interest in hypergraph neural networks (HGNNs) is driven by their capacity to capture the complex relationships and patterns within hypergraph structured data across various domains, including computer vision, complex networks, and natural language processing. This paper comprehensively reviews recent advances in HGNNs and presents a taxonomy of mainstream models based on their architectures: hypergraph convolutional networks (HGCNs), hypergraph attention networks (HGATs), hypergraph autoencoders (HGAEs), hypergraph recurrent networks (HGRNs), and deep hypergraph generative models (DHGGMs). For each category, we delve into its practical applications, mathematical mechanisms, literature contributions, and open problems. Finally, we discuss some common challenges and promising research directions.This paper aspires to be a helpful resource that provides guidance for future research and applications of HGNNs.