Consensus convergence in large-group social network environment: Coordination between trust relationship and opinion similarity

Group decision-making (GDM) in large-group social network environment (LGSNE) has attracted considerable attention in the field of decision science. Social relationships exist among decision-makers, and individual decisions are often influenced by others they are connected with. Opinions among large-scale decision-makers can easily be controversial and conflicting. Reaching consensus is necessary, but it requires the adjustment of some individual opinions. Due to differences in self-interest and perception, some decision-makers are noncooperative with regard to adjusting their opinions to promote consensus. This may delay consensus convergence and ultimately affect decision quality. This study proposes a two-dimensional consensus convergence model considering noncooperative behaviors. We first describe the characteristics of GDM problems in LGSNE. Two measurement attributes – trust relationship and opinion similarity – are identified as important factors throughout the decision-making process. Then, we propose a hierarchical clustering method based on the trust–similarity measure. A weight-determining method for clusters is presented that considers the internal and external features of a cluster. Based on these, a two-dimensional consensus convergence process is designed to reduce opinion differences and manage noncooperative behaviors. Finally, a numerical experiment is used to illustrate the feasibility and efficacy of the proposed model, and comparative analysis reveals its features and advantages.