A Minimum-Cost Consensus Model in Social Networks Derived From Uncertain Preferences
研究提出一种基于信任关系和调整成本的社交网络共识方法，通过信任传播、权重分配和共识达成三阶段，利用机会约束规划模型处理不确定偏好，以最小化共识成本。
The influence of mutual interaction behaviors on the opinions and consensus process has gradually emerged due to the information sharing in social networks. Currently, the cost of making individual decisions and the similarity between experts’ decision behaviors are relatively less addressed in the social network decision process. Therefore, in this study, a consensus approach with the trust relationships and adjustment cost is proposed to fill in such a gap. The method is divided into three stages: 1) trust propagation; 2) weight allocation; and 3) consensus reaching. In the trust propagation phase, uninorm is extended to the uncertain theory and employed in the transmission and integration problems of trust relationships. In the weight allocation stage, the comprehensive weight is assigned based on network structure and strength of relationship. In the consensus-reaching process, two levels of consensus are considered: 1) consensus among individuals and 2) consensus between individuals and the collective group, and chance-constrained programming models are constructed to obtain collective decision opinions. Moreover, a comparative analysis is performed to clarify the effectiveness and advancement of the proposed consensus method.