ArticleScience advances2023
Community detection in large hypergraphs.
Article in Science advances, 2023. 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 abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Information theory for hypergraph similarity.Science advances · 2026Article
- Broad spectrum structure discovery in large-scale higher-order networks.Nature communications · 2026Article
- Hypergraph-based decay analysis for predicting and classifying states beyond regge trajectories.PloS one · 2026Article
- The microscale organization of directed hypergraphs.Communications physics · 2026Article
- Higher-order interactions shape collective human behaviour.Nature human behaviour · 2025Review
- Community detection in hypergraphs through hyperedge percolation.Scientific reports · 2025Article
- Structure and inference in hypergraphs with node attributes.Nature communications · 2024Article
- Community detection in hypergraphs via mutual information maximization.Scientific reports · 2024Article
- Patterns in Temporal Networks with Higher-Order Egocentric Structures.Entropy (Basel, Switzerland) · 2024Article
- The simpliciality of higher-order networks.EPJ data science · 2024Article
- Hyperedge prediction and the statistical mechanisms of higher-order and lower-order interactions in complex networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypergraphs, describing networks where interactions take place among any number of units, are a natural tool to model many real-world social and biological systems. Here, we propose a principled framework to model the organization of higher-order data. Our approach recovers community structure with accuracy exceeding that of currently available state-of-the-art algorithms, as tested in synthetic benchmarks with both hard and overlapping ground-truth partitions. Our model is flexible and allows capturing both assortative and disassortative community structures. Moreover, our method scales orders of magnitude faster than competing algorithms, making it suitable for the analysis of very large hypergraphs, containing millions of nodes and interactions among thousands of nodes. Our work constitutes a practical and general tool for hypergraph analysis, broadening our understanding of the organization of real-world higher-order systems.
Identifiers
What OpenQuestion holds
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.