A hybrid opinion dynamics model with leaders and followers fusing dynamic social networks in large-scale group decision-making

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Information Fusion Pub Date : 2024-11-20 DOI:10.1016/j.inffus.2024.102799
Yufeng Shen , Xueling Ma , Muhammet Deveci , Enrique Herrera-Viedma , Jianming Zhan
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Abstract

Objectives:

In this study, our goal is to enhance consensus efficiency in complex decision-making scenarios by constructing a large-scale group decision-making (LSGDM) method that integrates dynamic social network (DSN) and opinion dynamics. To this end, we design a model that can effectively cluster experts and dynamically adjust the network structure to more accurately reflect the diversity and complexity of the actual decision-making process.

Methods:

Specifically, we first design an improved Louvain algorithm based on social influence to effectively cluster participants with similar opinions into the same community. Then, we utilize structural hole theory to distinguish opinion leaders and followers in the community, and construct a DSN updating mechanism based on opinion disagreement and trust relationship. Finally, we combine the advantages of the DeGroot and Hegselmann–Krause (HK) models and propose a hybrid opinion dynamics (HOD) model in the LSGDM framework, referred to as DSN-HOD-LSGDM.

Findings:

Experimental results demonstrate that the DSN-HOD-LSGDM model significantly enhances consensus-building efficiency across diverse decision-making communities. The model effectively tracks opinion evolution in complex networks, outperforming conventional methods in both adaptability and scalability.

Novelty:

In this study, we propose an improved Louvain algorithm and dynamic weight allocation mechanism based on influence index, and design a personalized opinion evolution mechanism combined with structural hole theory. By fusing opinion evolution and dynamic trust, we construct a new LSGDM consensus model that realizes the dynamic adjustment of the trust relationship between individuals.
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大规模群体决策中融合动态社交网络的领导者与追随者混合舆论动力学模型
在本研究中,我们的目标是通过构建一种整合了动态社会网络(DSN)和意见动态的大规模群体决策(LSGDM)方法,提高复杂决策场景中的共识效率。为此,我们设计了一个模型,可以有效地对专家进行聚类,并动态调整网络结构,以更准确地反映实际决策过程的多样性和复杂性。
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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