mlsanityData to 7 Oct 2026

Message Passing Neural Networks for Hypergraphs

arXiv 2203.16995 ↗First posted 31 Mar 2022cs.LGcited by 13

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Abstract (arXiv)

Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing capable of processing hypergraph-structured data. We show that the proposed model defines a design space for neural network models for hypergraphs, thus generalizing existing models for hypergraphs. We report experiments on a benchmark dataset for node classification, highlighting the effectiveness of the proposed model with respect to other state-of-the-art methods for graphs and hypergraphs. We also discuss the benefits of using hypergraph representations and, at the same time, highlight the limitation of using equivalent graph representations when the underlying problem has relations among more than two objects.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 31 Mar 2022.

Builds on

14 of 19 references.

References · 14

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  4. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  6. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  7. 2018Link Prediction Based on Graph Neural Networkscited by 829
  8. 2018Hypergraph Neural Networkscited by 482
  9. 2015Diffusion-Convolutional Neural Networkscited by 268
  10. 2019Hypergraph Convolution and Hypergraph Attentioncited by 226
  11. 2020Design Space for Graph Neural Networkscited by 182
  12. 2018GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphscited by 164
2 more
  1. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 145
  2. 2021You are AllSet: A Multiset Function Framework for Hypergraph Neural Networkscited by 124

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 4; 2025: 3; 2026: 1.

13 papers.

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  1. 2023Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networkscited by 43
  2. 2024E(n) Equivariant Topological Neural Networkscited by 22
  3. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  4. 2023Simplicial Representation Learning with Neural k-Formscited by 18
  5. 2023From Hypergraph Energy Functions to Hypergraph Neural Networkscited by 17
  6. 2023ICML 2023 Topological Deep Learning Challenge : Design and Resultscited by 7
  7. 2025Recent Advances in Hypergraph Neural Networkscited by 7
  8. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  9. 2025Topotein: Topological Deep Learning for Protein Representation Learningcited by 2
  10. 2023Momentum Gradient-based Untargeted Attack on Hypergraph Neural Networkscited by 1
  11. 2024Learning From Simplicial Data Based on Random Walks and 1D Convolutionscited by 0
  12. 2025Higher-Order Graph Databasescited by 0
1 more
  1. 2026From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networkscited by 0