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Irreversible opinion spreading on scale-free networks.
1Consortium of the Americas for Interdisciplinary Science and Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico 87131, USA. jcandia@nd.edu
Summary
This study reveals hubs drive opinion spreading on scale-free networks. Unlike equilibrium models, this nonequilibrium model exhibits persistent order-disorder phase transitions, even in large systems.
Area of Science:
- Statistical Physics
- Network Science
- Computational Physics
Background:
- Opinion spreading models are crucial for understanding social dynamics.
- Scale-free networks, like Barabási-Albert networks, exhibit complex structures with influential 'hubs'.
- Nonequilibrium models offer unique insights into system dynamics not captured by equilibrium counterparts.
Purpose of the Study:
- To investigate the dynamical and critical behavior of irreversible opinion spreading on Barabási-Albert scale-free networks.
- To explore the role of network topology, specifically hubs, in opinion dynamics.
- To compare the critical behavior of this nonequilibrium model with equilibrium spin systems.
Main Methods:
- Extensive Monte Carlo simulations were employed to model opinion spreading.
- The magnetic Eden model, a nonequilibrium kinetic growth model, was utilized.
- Finite-size scaling procedures were applied to analyze phase transitions.
Main Results:
- Hubs play a leading role in the opinion spreading process on Barabási-Albert networks.
- The model exhibits temperature-dependent growth, leading to ordered or disordered states in finite systems.
- Effective order-disorder phase transitions persist in the thermodynamic limit, unlike equilibrium Ising models on similar networks.
Conclusions:
- Irreversible opinion spreading on scale-free networks displays distinct critical behavior compared to equilibrium models.
- The findings highlight the importance of network structure and nonequilibrium dynamics in social phenomena.
- The study provides a deeper understanding of phase transitions in complex systems.
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