Consensus Convergence Speed in Social Network DeGroot Model: The Effects of the Agents With High Self-Confidence Levels

被引：13

作者：

Ding, Zhaogang ^{[1
]}

Chen, Xia ^{[2
]}

Dong, Yucheng ^{[2
]}

Yu, Shui ^{[3
]}

Herrera, Francisco ^{[4
,5
]}

机构：

[1] Northwest Univ, Sch Publ Management Emergency Management, Xian 710069, Peoples R China

[2] Sichuan Univ, Business Sch, Chengdu 610065, Peoples R China

[3] Univ Technol Sydney, Sch Comp Sci, Ultimo, NSW 2007, Australia

[4] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, E-18071 Granada, Spain

[5] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah 21589, Saudi Arabia

来源：

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS | 2023年 / 10卷 / 05期

基金：

中国博士后科学基金;

关键词：

Social networking (online); Convergence; Eigenvalues and eigenfunctions; Computational modeling; Matrix decomposition; Directed graphs; Decision making; Consensus convergence speed; opinion dynamics; self-confidence; social networks; BOUNDED CONFIDENCE; DYNAMICS;

D O I：

10.1109/TCSS.2022.3191468

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In group decision making (GDM), opinion dynamics is a useful tool to investigate consensus formation. Notably, consensus convergence speed is of key importance to manage the consensus formation in GDM with opinion dynamics. Recently, social network DeGroot (SNDG) model has been widely used in opinion dynamics. Based on this, this article dedicates to study how agents' high self-confidence levels affect the consensus convergence speed in SNDG model. Interestingly, using theoretical analysis, we prove that: 1) the speed of consensus reaching is subject to the largest self-confidence level of opinion followers and 2) the speed of consensus reaching is also subject to the top two self-confidence levels of opinion leaders. Furthermore, through extensive simulation', we find that the theoretical results are robust to the topological structure and the size of social networks.

引用

页码：2882 / 2892

页数：11

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