Phase transition in the majority-vote model on time-varying networks
Bing Wang1, Xu Ding1, Yuexing Han1,2
1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, People's Republic of China.
Physical Review. E
|February 23, 2022
Summary
This study examines opinion dynamics on evolving social networks. The undirected process, considering both active and passive interactions, achieves greater consensus than the single directed process.
Area of Science:
- Complex Systems
- Sociophysics
- Network Science
Background:
- Social interactions influence opinion formation.
- Existing models often use static networks, neglecting real-world dynamic interactions.
- Temporal networks and dynamic interactions are crucial for realistic opinion modeling.
Purpose of the Study:
- To investigate the majority-vote (MV) model on temporal networks.
- To analyze the impact of network temporality on opinion dynamics.
- To compare opinion update processes considering active and passive interactions.
Main Methods:
- Utilized the activity-driven time-varying network with attractiveness (ADA) model for network evolution.
- Developed and analyzed single directed (SD) and undirected (UD) opinion update processes.
- Employed mean-field theory and numerical simulations to derive critical thresholds.
- Validated findings on real-world network data.
Main Results:
- Derived critical noise thresholds for both SD and UD processes.
- The UD process demonstrated a higher consensus level than the SD process under identical noise conditions.
- Temporality significantly impacts opinion dynamics in social networks.
- Model predictions were confirmed through simulations and real network analysis.
Conclusions:
- Opinion dynamics are significantly influenced by the temporal nature of social networks.
- The undirected interaction model, incorporating diverse connection types, promotes greater social consensus.
- Findings provide insights into opinion formation in dynamic, real-world social systems.
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