ArticleNature communications2022
Inference of hyperedges and overlapping communities in hypergraphs.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed.
- Key Node Identification in Spatial Information Networks via Soft Community Partition and Hypergraph Interweaving Degree.Sensors (Basel, Switzerland) · 2026Article
- PVAED: prior-guided variational autoencoders with diffusion denoising for interpretable single-cell representation learning.Briefings in bioinformatics · 2026Article
- Broad spectrum structure discovery in large-scale higher-order networks.Nature communications · 2026Article
- Reducibility of higher-order networks from dynamics.Nature communications · 2026Article
- The microscale organization of directed hypergraphs.Communications physics · 2026Article
- Higher-order interactions shape collective human behaviour.Nature human behaviour · 2025Review
- Mining higher-order triadic interactions.Nature communications · 2025Article
- HEProOE: A hyperedge enhanced probabilistic optimal estimation method for detecting spatial fuzzy communities.Scientific reports · 2025Article
- Community detection in hypergraphs through hyperedge percolation.Scientific reports · 2025Article
- A causal framework for the drivers of animal social network structure.PLoS computational biology · 2025Article
- Emergence of cooperation promoted by higher-order strategy updates.PLoS computational biology · 2025Article
- Revealing non-trivial information structures in aneural biological tissues via functional connectivity.PLoS computational biology · 2025Article
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Structure and inference in hypergraphs with node attributes.Nature communications · 2024Article
- The Ihara zeta function as a partition function for network structure characterisation.Scientific reports · 2024Article
- Higher-order correlations reveal complex memory in temporal hypergraphs.Nature communications · 2024Article
- Legal hypergraphs.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
- Patterns in Temporal Networks with Higher-Order Egocentric Structures.Entropy (Basel, Switzerland) · 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
- Partial entropy decomposition reveals higher-order information structures in human brain activity.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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to characterize the structural organization of hypergraphs. The method allows to infer missing hyperedges of any size in a principled way, and to jointly detect overlapping communities in presence of higher-order interactions. Furthermore, our model has an efficient numerical implementation, and it runs faster than dyadic algorithms on pairwise records projected from higher-order data. We apply our method to a variety of real-world systems, showing strong performance in hyperedge prediction tasks, detecting communities well aligned with the information carried by interactions, and robustness against addition of noisy hyperedges. Our approach illustrates the fundamental advantages of a hypergraph probabilistic model when modeling relational systems with higher-order interactions.
Indexed as
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.