Consensus reaching in social network DeGroot Model: The roles of the Self-confidence and node degree

被引：86
作者：
Ding, Zhaogang [1 ]
Chen, Xia [2 ]
Dong, Yucheng [2 ]
Herrera, Francisco [3 ,4 ]
机构：
[1] Northwest Univ, Sch Publ Management, Xian 710069, Shaanxi, Peoples R China
[2] Sichuan Univ, Business Sch, Chengdu 610065, Sichuan, Peoples R China
[3] Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain
[4] King Abdulaziz Univ, Fac Comp & Informat Technol, North Jeddah, Saudi Arabia
来源：
INFORMATION SCIENCES | 2019年 / 486卷
关键词：
Consensus; Opinion dynamics; Self-confidence; Node degree; Convergence speed; GROUP DECISION-MAKING; OPINION DYNAMICS; FUSION PROCESS; SZNAJD MODEL; MINIMUM-COST; EMERGENCE; SYSTEMS; SPREAD;
D O I：
10.1016/j.ins.2019.02.028
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we investigate how the agent's self-confidence level and the node degree influence the consensus opinion formation and the consensus convergence speed in the social network DeGroot model. We find that (1) the higher self-confidence will increase the agent's importance degree to determine the consensus opinion, but will also slow down the convergence speed for all agents to be able to obtain consensus, and (2) it is conducive to accelerating the convergence speed to be able to reach a consensus where all agents can manage to balance self-confidence levels and node degrees in the social network. (C) 2019 Elsevier Inc. All rights reserved.
引用
收藏
页码：62 / 72
页数：11
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