Social relation-driven consensus reaching in large-scale group decision-making using semi-supervised classification

The fundamental goal of group decision making (GDM) is to improve consensus among experts and reduce individual conflicts of interest in the process of alternative selection. By analyzing the social network relationships of decision-makers (DMs), such as trust and preference similarity relationships, more effective consensus-reaching mechanisms can be designed. Previous studies developed supervised classification algorithms for DM groups of more than 1, 000 participants on social networks. However, in large-scale social networks, the social connection relations of DMs cannot be completely investigated as human resources and costs during the decision-making process impose limitations. These DMs cannot be effectively trained to build a classification model, which is referred to as unlabelled DMs and results in a large amount of information being ignored during the model training process, leading to an unfair process that can deepen the conflict among experts. To address the information loss problem, this study proposes a semi-supervised learning model for social networks with incomplete trust relations, aiming to effectively absorb the preference information of unlabelled DMs into the classification model, thereby improving the effectiveness of classification management. Specifically, the cost-sensitive semi-supervised support vector machine (SVM) introduced in the inner product space is subsequently used by the DMs' classification model to accurately divide unlabelled DMs. A minimum cost adjustment consensus model is then constructed based on the DM subgroups. Numerical experiments on urban renewal were conducted to verify the effectiveness of the proposed method. The empirical and simulation results demonstrated that the proposed method can decrease total consensus costs for social-network GDM with missing trust-relationship information.