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Opinion formation on adaptive networks with intensive average degree
B Schmittmann1, Abhishek Mukhopadhyay
1Department of Physics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0435, USA.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 15, 2011
Summary
This study models opinion dynamics on adaptive networks. We found two consensus states and one metastable state where opinions split evenly, with relaxation time growing exponentially with network size.
Area of Science:
- Complex systems
- Network science
- Sociophysics
Background:
- Understanding how opinions spread and stabilize in social networks is crucial.
- Previous models often lacked system-size independence in network connectivity.
Purpose of the Study:
- To investigate the evolution of binary opinions on adaptive networks.
- To analyze steady states and relaxation dynamics in relation to network size.
- To address limitations in prior adaptive network models.
Main Methods:
- Simulations and analytical arguments were employed.
- Nodes update opinions based on local majority.
- Links are rewired based on opinion similarity (p̃, q̃ = O(1/N)).
Main Results:
- Identified two absorbing states corresponding to perfect consensus.
- Observed one metastable state with an even split of opinions.
- The relaxation time for the metastable state exhibits exponential growth with system size (N).
Conclusions:
- The model exhibits robust consensus formation.
- The identified metastable state's long relaxation time highlights potential for persistent polarization.
- A previously reported metastable state is absent in this size-independent model.
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