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Higher-order correlations reveal complex memory in temporal hypergraphs

Author

Listed:
  • Luca Gallo

    (Central European University)

  • Lucas Lacasa

    (CSIC-UIB)

  • Vito Latora

    (Queen Mary University of London
    University of Catania
    INFN Sezione di Catania
    Complexity Science Hub Vienna)

  • Federico Battiston

    (Central European University)

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.

Suggested Citation

  • Luca Gallo & Lucas Lacasa & Vito Latora & Federico Battiston, 2024. "Higher-order correlations reveal complex memory in temporal hypergraphs," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48578-6
    DOI: 10.1038/s41467-024-48578-6
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    References listed on IDEAS

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    Cited by:

    1. Iacopo Iacopini & Márton Karsai & Alain Barrat, 2024. "The temporal dynamics of group interactions in higher-order social networks," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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