ArticleScience advances2024
Learning the effective order of a hypergraph dynamical system.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Hypernetworks induce stable hyperlocking.Nature communications · 2026Article
- Reducibility of higher-order networks from dynamics.Nature communications · 2026Article
- Higher-order interactions shape collective human behaviour.Nature human behaviour · 2025Review
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Not your private tête-à-tête: leveraging the power of higher-order networks to study animal communication.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Connectivity of Random Geometric Hypergraphs.Entropy (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamical systems on hypergraphs can display a rich set of behaviors not observable for systems with pairwise interactions. Given a distributed dynamical system with a putative hypergraph structure, an interesting question is thus how much of this hypergraph structure is actually necessary to faithfully replicate the observed dynamical behavior. To answer this question, we propose a method to determine the minimum order of a hypergraph necessary to approximate the corresponding dynamics accurately. Specifically, we develop a mathematical framework that allows us to determine this order when the type of dynamics is known. We use these ideas in conjunction with a hypergraph neural network to directly learn the dynamics itself and the resulting order of the hypergraph from both synthetic and real datasets consisting of observed system trajectories.
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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.