ArticleEPJ data science2024
The simpliciality of higher-order networks.
Article in EPJ data science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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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.
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Who cites it
9 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
- A Blue Start: A large-scale pairwise and higher-order social network dataset.Scientific data · 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
- Community detection in hypergraphs through hyperedge percolation.Scientific reports · 2025Article
- Hyperedge overlap drives explosive transitions in systems with higher-order interactions.Nature communications · 2025Article
- Higher-order correlations reveal complex memory in temporal hypergraphs.Nature communications · 2024Article
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
Abstract
Higher-order networks are widely used to describe complex systems in which interactions can involve more than two entities at once. In this paper, we focus on inclusion within higher-order networks, referring to situations where specific entities participate in an interaction, and subsets of those entities also interact with each other. Traditional modeling approaches to higher-order networks tend to either not consider inclusion at all (e.g., hypergraph models) or explicitly assume perfect and complete inclusion (e.g., simplicial complex models). To allow for a more nuanced assessment of inclusion in higher-order networks, we introduce the concept of "simpliciality" and several corresponding measures. Contrary to current modeling practice, we show that empirically observed systems rarely lie at either end of the simpliciality spectrum. In addition, we show that generative models fitted to these datasets struggle to capture their inclusion structure. These findings suggest new modeling directions for the field of higher-order network science.
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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.