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Area of Science:

  • Complex systems
  • Statistical physics
  • Network science

Background:

  • Previous studies on particle systems often assumed small group sizes for interactions.
  • Polyadic (higher-order) interactions are known to influence system dynamics but are less explored with varying group scales.

Purpose of the Study:

  • To investigate the impact of multiscale polyadic group interactions (small and large groups) on system dynamics.
  • To analyze the effects on equilibrium dynamics and phase transitions in paradigmatic models.

Main Methods:

  • Mean-field analysis of two models: SIS-epidemic and adaptive voter model.
  • Examination of how varying group sizes in polyadic interactions affect model outcomes.

Main Results:

  • For the SIS-epidemic model, a bistability region was identified, protecting the disease-free state beyond the typical epidemic threshold.
  • For the adaptive voter model, multiscale polyadic interactions were shown to stabilize the network or accelerate convergence to an unbiased equilibrium.

Conclusions:

  • Multiscale polyadic interactions introduce significant effects not captured by small-group assumptions.
  • These findings have implications for understanding disease spread and network behavior in complex systems.