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Animal social networks with weighted interactions can alter collective motion. Weights generally decrease flocking thresholds, increasing fragility, but specific configurations can enhance resilience to noise.

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Area of Science:

  • Complex systems
  • Collective behavior
  • Network science

Background:

  • Traditional models of self-organized collective motion often use non-weighted interactions.
  • Animal social structures involve complex patterns of interaction weights.
  • Understanding how weighted interactions influence collective phenomena is crucial.

Purpose of the Study:

  • To investigate the impact of weighted social networks on flocking transitions in collective motion models.
  • To explore how network heterogeneity and weight correlations affect flocking behavior.
  • To determine if weighted networks can be tuned for enhanced resilience to noise.

Main Methods:

  • Developed and analyzed variations of collective motion models with static weighted interaction networks.
  • Employed heterogeneous mean-field theory for phase diagram analysis in a 1D model.
  • Validated theoretical predictions using computer simulations for both 1D and 2D (Vicsek model) scenarios, including real animal social network data.

Main Results:

  • Identified distinct phases based on network heterogeneity and weight-degree correlations, including suppressed or absent flocking transitions.
  • Demonstrated that specific weight configurations can create a maximum threshold, enhancing resilience to noise.
  • Found that weights in real animal social networks generally reduce the flocking threshold, increasing fragility, dependent on weight pattern heterogeneity.

Conclusions:

  • Weighted animal social networks significantly alter collective motion dynamics compared to non-weighted systems.
  • Network properties, particularly weight heterogeneity, critically influence the flocking transition and system stability.
  • Tuning interaction weights offers a potential mechanism to control collective behavior and resilience in biological and artificial systems.