Social network large-scale decision-making (SN-LSDM) has attracted much attention in the field of decision science. Clustering and consensus building are two important processes for solving SN-LSDM problems. Traditional clustering methods are typically based on the opinion similarity. When extending decision-making to social network contexts, we argue that trust relationships among decision-makers (DMs) serve as a reliable resource for clustering. This study proposes a trust Cop-Kmeans clustering algorithm, which is a semisupervised clustering technique using prior knowledge of trust constraints generated by trust relationships. The consensus-reaching process (CRP) is an effective tool for reducing differences of opinion. In general, the costs and resources associated with the CRP are limited. Therefore, minimum-cost consensus (MCC) models have been developed and used widely in various group decision-making contexts. As an important resource influencing decision-making, trust provides a common-sense perception that the opinion of a DM with a high trust degree is considered to be widely recognized by others. We hold that the DM with high trust and low consensus can reduce the consensus cost by voluntarily losing some trust. Consequently, an improved MCC model considering voluntary trust loss is developed. Finally, a numerical example is presented to illustrate the feasibility of the proposed clustering algorithm and consensus model. A comparative analysis is implemented to explore the influence of the trust constraint on clustering and the influence of trust loss on the CRP.

As a widely encountered decision-making scenario in modern society, social network large-scale decision-making (SNLSDM) is becoming a frontier topic in the field of decision science. Decision information usually includes social relationships among decision-makers (DMs) and individual opinions. This makes clustering and consensus building, which are two important processes for solving SNLSDM problems, significantly complex and requires the integration of multiple factors. This study designs a decision support method that consists of a constrained community detection (CCD) method and a multistage multicost consensus (MSMulCC) model. The CCD method takes the similarities among individual opinions as the mandatory constraint to guide the classification of DMs based on social relationships. The consensus-reaching process (CRP) is an effective tool for reducing differences of opinion. We hold that the DM with high compatibility but low consensus can reduce the adjustment amount by actively losing some compatibility. In this way, three types of consensus costs are generated, including individual adjustment cost, group adjustment cost, and compatibility loss cost. In this study, an MSMulCC model is developed, and the impact of different types of consensus costs on CRP. Finally, the feasibility and characteristics of the proposal are revealed through a case study and comparative analysis of the supply chain financing model selection.