ArticleIEEE transactions on neural networks and learning systems2024
A Survey on Hyperlink Prediction.
Article in IEEE transactions on neural networks and learning systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- Hypergraph learning with multi-dimensional metabolite feature extractions and static-dynamic attention mechanisms to fill missing reactions in metabolic networks.Briefings in bioinformatics · 2026Article
- CCLPH: community-centric approach to link prediction in hyper complex networks.Scientific reports · 2026Article
- A Novel Topology-Based Candidate Reaction Prediction Approach for Gap-Fillings of Genome-Scale Metabolic Models.Metabolites · 2026Article
- A multi-way SMILES-based hypergraph inference network for metabolic model reconstruction.Communications biology · 2026Article
- Automated biomedical hypothesis generation with time-aware hypergraph contrastive learning.Knowledge and information systems · 2026Article
- Enriched knowledge representation in biological fields: a case study of literature-based discovery in Alzheimer's disease.Journal of biomedical semantics · 2025Article
- Heterogeneous entity representation for medicinal synergy prediction.Bioinformatics (Oxford, England) · 2024Article
- Inconsistency among evaluation metrics in link prediction.PNAS nexus · 2024Article
- Stability of Ecological Systems: A Theoretical Review.Physics reports · 2024Article
- Link Prediction in Dynamic Social Networks Combining Entropy, Causality, and a Graph Convolutional Network Model.Entropy (Basel, Switzerland) · 2024Article
- Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling.Entropy (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
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 and social communication networks to protein-protein interaction networks. In this article, we provide a systematic and comprehensive survey on hyperlink prediction. We adopt a classical taxonomy from link prediction to classify the 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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.