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This study analyzes consensus dynamics on activity-driven temporal networks. Temporal network structures significantly impact consensus dynamics, revealing interesting symmetries and differences compared to static networks.

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Area of Science:

  • Complex Systems
  • Network Science
  • Statistical Physics

Background:

  • Consensus dynamics are crucial for understanding collective behavior in various systems.
  • Temporal networks, with their time-varying structure, offer a more realistic model for social and biological interactions.
  • Activity-driven networks incorporate agent behavior in network formation, influencing dynamics.

Purpose of the Study:

  • To investigate generalized consensus dynamics on activity-driven temporal networks.
  • To analyze the influence of agent activity and attractiveness on consensus achievement.
  • To evaluate consensus time and exit probabilities in these dynamic networks.

Main Methods:

  • Development of a heterogeneous mean-field approach.
  • Derivation of a differential equation for the average density of agents in a specific state.
  • Analysis of consensus time and exit probability.

Main Results:

  • A differential equation was derived to model consensus dynamics.
  • The study evaluated average consensus time and exit probability.
  • A symmetry was observed between voter and Moran dynamics in specific network types.

Conclusions:

  • Temporal network structure significantly impacts consensus dynamics.
  • Agent activity and attractiveness play key roles in consensus formation.
  • The findings highlight differences between temporal and static network dynamics.