ArticleNature communications2024
Higher-order correlations reveal complex memory in temporal hypergraphs.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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.
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Who cites it
12 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
- Persistent cycles and network resilience: a hypernetwork-based framework for temporal graph analysis.Scientific reports · 2026Article
- The microscale organization of directed hypergraphs.Communications physics · 2026Article
- Higher-order interactions shape collective human behaviour.Nature human behaviour · 2025Review
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Drivers of cooperation in social dilemmas on higher-order networks.Journal of the Royal Society, Interface · 2025Article
- Social polarization promoted by sparse higher-order interactions.Communications physics · 2025Article
- Biological detail and graph structure in network neuroscience.Frontiers in network physiology · 2025Review
- Higher-order temporal network prediction and interpretation.PloS one · 2025Article
- The temporal dynamics of group interactions in higher-order social networks.Nature communications · 2024Article
- Patterns in Temporal Networks with Higher-Order Egocentric Structures.Entropy (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics, as they are based on pairwise interactions only. Here, we use time-varying hypergraphs to describe such systems, and we introduce a framework based on higher-order correlations to characterize their temporal organization. The analysis of human interaction data reveals the existence of coherent and interdependent mesoscopic structures, thus capturing aggregation, fragmentation and nucleation processes in social systems. We introduce a model of temporal hypergraphs with non-Markovian group interactions, which reveals complex memory as a fundamental mechanism underlying the emerging pattern in the data.
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.