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Distributed opinion competition scheme with gradient-based neural network in social networks.
Zhuowen Feng1, Yuru Xing2, Guancheng Wang3
1College of Literature and News Communication, Guangdong Ocean University, ZhanJiang, 524088, China.
Scientific Reports
|December 27, 2024
Summary
This study introduces a new model for opinion competition in social networks, showing how certain opinions become dominant. It offers insights into predicting viewpoint outcomes in complex social systems.
Area of Science:
- Computational Social Science
- Network Science
- Opinion Dynamics
Background:
- Social networks are central to information spread and public discussion.
- Understanding opinion competition and consensus formation is crucial.
Purpose of the Study:
- To introduce a novel distributed competition model for opinion dynamics in social networks.
- To analyze the evolution and prevalence of opinions within social environments.
Main Methods:
- Quantifying social network elements as parameters to represent opinion dynamics.
- Employing a modified gradient-based neural network for opinion evolution.
- Conducting simulations of real-world competitive scenarios.
Main Results:
- A subset of opinions is identified as ultimately prevailing and adopted.
- The model demonstrates stability and convergence through theoretical analysis.
- Experiments validate the model's practical applications.
Conclusions:
- The proposed model provides a structured approach to understanding opinion dynamics.
- It enhances the analysis and prediction of competitive behaviors and viewpoint outcomes.
- The model has broad applicability across various social network phenomena.
Keywords:
k-winners-take-all (k-WTA)Distributed schemeOpinion competitionRecurrent neural network (RNN)Social networkMore Related Videos
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