Social network clustering and consensus-based distrust behaviors management for large-scale group decision-making with incomplete hesitant fuzzy preference relations

• Published in: Applied soft computing Vol. 117; p. 108373
• Main Authors: Lu, Yanling, Xu, Yejun, Huang, Jing, Wei, Ju, Herrera-Viedma, Enrique
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022
• Subjects: Consensus; Distrust behaviors; Incomplete hesitant fuzzy preference relation; Large-scale group decision-making; Social network; Social network; Incomplete hesitant fuzzy preference relation; Distrust behaviors; Consensus; Large-scale group decision-making
• DOI: 10.1016/j.asoc.2021.108373
• ISSN: 1568-4946

With the development of social network platforms, large-scale group decision-making in social network (LSGDM-SN) has been formed. As decision makers (DMs) come from different fields and have complex individual backgrounds, which leads to their distrust in the moderator. Moreover, in LSGDM-SN, since DMs can hardly grasp all the information about the decision problem, the hesitant fuzzy preference relations (HFPRs) they have expressed may be incomplete. However, in current LSGDM-SN issues, the distrust behaviors and incomplete HFPRs have never been discussed simultaneously. In this context, this paper aims to propose a method to estimate incomplete values in HFPRs, and develop a consensus management process which considers distrust behaviors. This paper focuses on LSGDM-SN on the basis of social network clustering and consensus-based distrust behaviors management with incomplete HFPRs. In this paper, a social network clustering method based on grey clustering algorithm is proposed to classify the DMs with similar social clustering degree into a subset. Afterwards, a method including two situations is developed to estimate incomplete values in HFPRs. Furthermore, an identification mechanism is presented to detect the DMs’ distrust behaviors, and three modification strategies are provided for managing different types of distrust behaviors. In addition, a case study is given to illustrate the feasibility of the proposed method. Finally, comparative analysis and discussion are explored to verify the advantages of the proposed LSGDM-SN with incomplete HFPRs. •This paper develops an LSGDM-SN framework based on social network analysis to deal with distrust behaviors and incomplete HFPRs.•A social network clustering method based on grey clustering algorithm is proposed to improve the efficiency of LSGDM-SN problem.•Two situations for estimating missing values in incomplete HFPRs based on the value of opinion differences are proposed.•A mechanism for detecting and managing the distrust behaviors is presented.