# A Survey on Hyperlink Prediction

arXiv: https://arxiv.org/abs/2207.02911 · HTML: https://mlsanity.com/paper/2207.02911/

## 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 (most cited first, 30 of 41)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [Explainability Techniques for Graph Convolutional Networks](https://mlsanity.com/paper/1905.13686/) (2019)
- [HNHN: Hypergraph Networks with Hyperedge Neurons](https://mlsanity.com/paper/2006.12278/) (2020)
- [A Gentle Introduction to Deep Learning for Graphs](https://mlsanity.com/paper/1912.12693/) (2019)
- [Simplicial Closure and higher-order link prediction](https://mlsanity.com/paper/1802.06916/) (2018)
- [Hyper-SAGNN: a self-attention based graph neural network for hypergraphs](https://mlsanity.com/paper/1911.02613/) (2019)
- [HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs](https://mlsanity.com/paper/2010.04558/) (2020)
- [Structural Deep Embedding for Hyper-Networks](https://mlsanity.com/paper/1711.10146/) (2017)
- [Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks](https://mlsanity.com/paper/1311.1780/) (2013)
- [How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction](https://mlsanity.com/paper/2001.11181/) (2020)
- [Link Prediction in Social Networks: the State-of-the-Art](https://mlsanity.com/paper/1411.5118/) (2014)
- [Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion](https://mlsanity.com/paper/2005.04843/) (2020)
- [Learnable Hypergraph Laplacian for Hypergraph Learning](https://mlsanity.com/paper/2106.05701/) (2021)
- [HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network](https://mlsanity.com/paper/2206.12747/) (2022)
- [Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks](https://mlsanity.com/paper/2101.07773/) (2021)
- [AHP: Learning to Negative Sample for Hyperedge Prediction](https://mlsanity.com/paper/2204.06353/) (2022)
- [Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs](https://mlsanity.com/paper/2002.03392/) (2020)

## Built on by (most cited first, 9 of 9)

- [Training-Free Message Passing for Learning on Hypergraphs](https://mlsanity.com/paper/2402.05569/) (2024)
- [CAT-Walk: Inductive Hypergraph Learning via Set Walks](https://mlsanity.com/paper/2306.11147/) (2023)
- [Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data](https://mlsanity.com/paper/2211.14144/) (2022)
- [Hypergraph: A Unified and Uniform Definition with Application to Chemical Hypergraph and More](https://mlsanity.com/paper/2405.12235/) (2024)
- [Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking](https://mlsanity.com/paper/2402.11339/) (2024)
- [ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery](https://mlsanity.com/paper/2506.11041/) (2025)
- [HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks](https://mlsanity.com/paper/2410.02087/) (2024)
- [Learning Multi-Order Block Structure in Higher-Order Networks](https://mlsanity.com/paper/2511.21350/) (2025)
- [A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2607.16198/) (2026)

## 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.