Managing consensus based on community classification in opinion dynamics

Yuntian Zhang; Xiaoliang Chen; Zexia Huang; Xianyong Li; Yajun Du; Yuntian Zhang; Xiaoliang Chen; Zexia Huang; Xianyong Li; Yajun Du

doi:10.3934/nhm.2023035

Networks and Heterogeneous Media

2023, Volume 18, Issue 2: 813-841. doi: 10.3934/nhm.2023035

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Research article Special Issues

Managing consensus based on community classification in opinion dynamics

1.
School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan, 610039, China
2.
Department of Computer Science and Operations Research, University of Montreal, Montreal, H3C3J7, Canada

Received: 24 November 2022 Revised: 08 February 2023 Accepted: 20 February 2023 Published: 09 March 2023

Opinion dynamics in social networks are fast becoming an essential instrument for concentrating on the effect of individual choices on external public information. One of the main challenges in seeing the dynamics is reaching an opinion consensus acceptable to managers in a social network. This issue is referred to as a consensus-reaching process (CRP). Most studies of CRP focus only on network structure and ignore the effect of agent opinions. In addition, existing methods ignore the diversities between divided communities. How to synthesize individual opinions with community diversities to solve CRP issues has remained unclear. Using the DeGroot model for opinion control, this paper considers the effects of network structures and agent opinions when dividing communities, incorporating community classification and targeted opinion control strategies. First, a community classification enhancement approach is utilized, introducing the concept of ambiguous nodes and their division methods. Second, we separate all communities into three levels, $ Center $, $ Base $, and $ Fringe $, according to the logical regions for opinion control. Third, an edge expansion algorithm and three opinion control strategies are proposed based on the community levels, which can significantly reduce the time it takes for the network to reach a consensus. Finally, numerical analysis and comparison are given to verify the feasibility of the proposed opinion control strategy.
- opinion dynamics,
- social network,
- consensus strategy,
- leadership
Citation: Yuntian Zhang, Xiaoliang Chen, Zexia Huang, Xianyong Li, Yajun Du. Managing consensus based on community classification in opinion dynamics[J]. Networks and Heterogeneous Media, 2023, 18(2): 813-841. doi: 10.3934/nhm.2023035

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Abstract

Opinion dynamics in social networks are fast becoming an essential instrument for concentrating on the effect of individual choices on external public information. One of the main challenges in seeing the dynamics is reaching an opinion consensus acceptable to managers in a social network. This issue is referred to as a consensus-reaching process (CRP). Most studies of CRP focus only on network structure and ignore the effect of agent opinions. In addition, existing methods ignore the diversities between divided communities. How to synthesize individual opinions with community diversities to solve CRP issues has remained unclear. Using the DeGroot model for opinion control, this paper considers the effects of network structures and agent opinions when dividing communities, incorporating community classification and targeted opinion control strategies. First, a community classification enhancement approach is utilized, introducing the concept of ambiguous nodes and their division methods. Second, we separate all communities into three levels, $ Center $, $ Base $, and $ Fringe $, according to the logical regions for opinion control. Third, an edge expansion algorithm and three opinion control strategies are proposed based on the community levels, which can significantly reduce the time it takes for the network to reach a consensus. Finally, numerical analysis and comparison are given to verify the feasibility of the proposed opinion control strategy.

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