Trust based consensus model for social network in an incomplete linguistic information context

• Published in: Applied soft computing Vol. 35; pp. 827 - 839
• Main Authors: Wu, Jian, Chiclana, Francisco, Herrera-Viedma, Enrique
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2015
• Subjects: Incomplete linguistic information; Multiple criteria group decision making; Social network; Trust aggregation; Trust propagation; Visual feedback; Social network; Trust propagation; Multiple criteria group decision making; Incomplete linguistic information; Visual feedback; Trust aggregation
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.02.023

(A) Trust propagating aggregation and visual consensus model for MCGDM under incomplete information. (B) Visual feedback simulation: consensus levels before and after recommendations implemented by experts. [Display omitted] •A theoretical framework to build consensus within a networked social group is presented.•A novel trust propagation method is proposed to derive trust relationship from an incomplete connected trust network.•A visual feedback process including a recommendation mechanism to provide individualised advice is implemented.•The implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process. A theoretical framework to consensus building within a networked social group is put forward. This article investigates a trust based estimation and aggregation methods as part of a visual consensus model for multiple criteria group decision making with incomplete linguistic information. A novel trust propagation method is proposed to derive trust relationship from an incomplete connected trust network and the trust score induced order weighted averaging operator is presented to aggregate the orthopairs of trust/distrust values obtained from different trust paths. Then, the concept of relative trust score is defined, whose use is twofold: (1) to estimate the unknown preference values and (2) as a reliable source to determine experts’ weights. A visual feedback process is developed to provide experts with graphical representations of their consensus status within the group as well as to identify the alternatives and preference values that should be reconsidered for changing in the subsequent consensus round. The feedback process also includes a recommendation mechanism to provide advice to those experts that are identified as contributing less to consensus on how to change their identified preference values. It is proved that the implementation of the visual feedback mechanism guarantees the convergence of the consensus reaching process.