Fuzzy Opinion Dynamics-Driven Consensus Model Within Hybrid Mesoscale Structure in Social Trust Network

Abstract

Large-scale group decision making provides an effective way to obtain desirable group decision outcomes. Many studies have neglected the effect of opinion dynamics on the consensus reaching process (CRP) in the social trust network (STN) with a hybrid mesoscale structure. In addition, the application of communication theory to the CRP has rarely been considered. This article is devoted to building a fuzzy opinion dynamics-driven consensus model through the detection of hybrid mesoscale structures. First, this article proposes a novel mesoscale structure detection method that can detect both overlapping community structures and core–periphery structures in the STN. Then, to capture the opinion dynamics process under hybrid mesoscale structures, this article improves the opinion dynamics model by delineating five categories of nodes. Considering the dynamic changes of the CRP, a consensus feedback mechanism driven by opinion dynamics is proposed. In addition, the opinion adjustment is based on the “Spiral of Silence Theory.” Finally, the practicality of the proposed model is illustrated through an example. The superiority of the new detection algorithm and the efficiency of the novel consensus model are demonstrated through comparative analysis.