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

Affiliations
  • 1Network Science Institute, Northeastern University London, London, E1W 1LP, United Kingdom. iacopo.iacopini@nulondon.ac.uk.
  • 2Department of Physics, Northeastern University, Boston, MA, 02115, USA. iacopo.iacopini@nulondon.ac.uk.
  • 3Department of Network and Data Science, Central European University, A-1100, Vienna, Austria. iacopo.iacopini@nulondon.ac.uk.
  • 4Department of Network and Data Science, Central European University, A-1100, Vienna, Austria.
  • 5National Laboratory for Health Security, HUN-REN Alfréd Rényi Institute of Mathematics, 1053, Budapest, Hungary.
  • 6Aix Marseille Univ, Université de Toulon, CNRS, CPT, Turing Center for Living Systems, Marseille, France.

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August 27, 2024

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.

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