mlsanityData to 14 Oct 2026

A Survey on Hyperlink Prediction

arXiv 2207.02911 ↗First posted 6 Jul 2022cs.LG cs.SIcited by 9

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Abstract (arXiv)

As a natural extension of link prediction on graphs, hyperlink prediction aims for the inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than two nodes. Hyperlink prediction has applications in a wide range of systems, from chemical reaction networks, social communication networks, to protein-protein interaction networks. In this paper, we provide a systematic and comprehensive survey on hyperlink prediction. We propose a new taxonomy to classify existing hyperlink prediction methods into four categories: similarity-based, probability-based, matrix optimization-based, and deep learning-based methods. To compare the performance of methods from different categories, we perform a benchmark study on various hypergraph applications using representative methods from each category. Notably, deep learning-based methods prevail over other methods in hyperlink prediction.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 41 of its 121 references that are in the map. First posted on arXiv 6 Jul 2022.

Builds on

41 of 121 references.

References · 41

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
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  9. 2018Link Prediction Based on Graph Neural Networkscited by 832
  10. 2015LINE: Large-scale Information Network Embeddingcited by 582
  11. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 506
  12. 2018Hypergraph Neural Networkscited by 488
29 more
  1. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
  2. 2017Residual Gated Graph ConvNetscited by 346
  3. 2019Explainability Techniques for Graph Convolutional Networkscited by 161
  4. 2020HNHN: Hypergraph Networks with Hyperedge Neuronscited by 109
  5. 2019A Gentle Introduction to Deep Learning for Graphscited by 108
  6. 2018Simplicial Closure and higher-order link predictioncited by 93
  7. 2019Hyper-SAGNN: a self-attention based graph neural network for hypergraphscited by 69
  8. 2020HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphscited by 54
  9. 2017Structural Deep Embedding for Hyper-Networkscited by 33
  10. 2013Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networkscited by 32
  11. 2020How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Predictioncited by 31
  12. 2014Link Prediction in Social Networks: the State-of-the-Artcited by 22
  13. 2020Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansioncited by 22
  14. 2021Learnable Hypergraph Laplacian for Hypergraph Learningcited by 16
  15. 2022HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Networkcited by 16
  16. 2021Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Taskscited by 13
  17. 2022AHP: Learning to Negative Sample for Hyperedge Predictioncited by 12
  18. 2020Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphscited by 11
  19. 2021A nonlinear diffusion method for semi-supervised learning on hypergraphscited by 11
  20. 2017Graphons: A Nonparametric Method to Model, Estimate, and Design Algorithms for Massive Networkscited by 9
  21. 2021Principled Hyperedge Prediction with Structural Spectral Features and Neural Networkscited by 7
  22. 2020HPRA: Hyperedge Prediction using Resource Allocationcited by 5
  23. 2014Negative Link Prediction in Social Mediacited by 4
  24. 2019Tensor Entropy for Uniform Hypergraphscited by 4
  25. 2021Hyperedge Prediction using Tensor Eigenvalue Decompositioncited by 3
  26. 2019Multilinear Time Invariant System Theorycited by 2
  27. 2020Hypergraph Partitioning using Tensor Eigenvalue Decompositioncited by 2
  28. 2021Predicting hyperlinks via hypernetwork loop structurecited by 2
  29. 2016Recovering Metabolic Networks using A Novel Hyperlink Prediction Methodcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 4; 2025: 2; 2026: 1.

9 papers.

  1. 2024Training-Free Message Passing for Learning on Hypergraphscited by 7
  2. 2023CAT-Walk: Inductive Hypergraph Learning via Set Walkscited by 6
  3. 2022Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Datacited by 5
  4. 2024Hypergraph: A Unified and Uniform Definition with Application to Chemical Hypergraph and Morecited by 3
  5. 2024Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breakingcited by 2
  6. 2025ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discoverycited by 1
  7. 2024HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networkscited by 0
  8. 2025Learning Multi-Order Block Structure in Higher-Order Networkscited by 0
  9. 2026A Survey on GNN-based Link Prediction: Techniques, Applications, and Challengescited by 0