Flocking dynamics mediated by weighted social networks

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.