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The temporal dynamics of group interactions in higher-order social networks

Author

Listed:
  • Iacopo Iacopini

    (Northeastern University London
    Northeastern University
    Central European University)

  • Márton Karsai

    (Central European University
    HUN-REN Alfréd Rényi Institute of Mathematics)

  • Alain Barrat

    (Université de Toulon, CNRS, CPT, Turing Center for Living Systems)

Abstract

Representing social systems as networks, starting from the interactions between individuals, sheds light on the mechanisms governing their dynamics. However, networks encode only pairwise interactions, while most social interactions occur among groups of individuals, requiring higher-order network representations. Despite the recent interest in higher-order networks, little is known about the mechanisms that govern the formation and evolution of groups, and how people move between groups. Here, we leverage empirical data on social interactions among children and university students to study their temporal dynamics at both individual and group levels, characterising how individuals navigate groups and how groups form and disaggregate. We find robust patterns across contexts and propose a dynamical model that closely reproduces empirical observations. These results represent a further step in understanding social systems, and open up research directions to study the impact of group dynamics on dynamical processes that evolve on top of them.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50918-5
    DOI: 10.1038/s41467-024-50918-5
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    References listed on IDEAS

    as
    1. Vedran Sekara & Sune Lehmann, 2014. "The Strength of Friendship Ties in Proximity Sensor Data," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-8, July.
    2. Iacopo Iacopini & Giovanni Petri & Alain Barrat & Vito Latora, 2019. "Simplicial models of social contagion," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    3. Alessandro Vespignani, 2018. "Twenty years of network science," Nature, Nature, vol. 558(7711), pages 528-529, June.
    4. Unai Alvarez-Rodriguez & Federico Battiston & Guilherme Ferraz Arruda & Yamir Moreno & Matjaž Perc & Vito Latora, 2021. "Evolutionary dynamics of higher-order interactions in social networks," Nature Human Behaviour, Nature, vol. 5(5), pages 586-595, May.
    5. 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.
    6. Petter Holme, 2015. "Modern temporal network theory: a colloquium," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(9), pages 1-30, September.
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