Trust propagation and trust network evaluation in social networks based on uncertainty theory

Uncertainty distributions can help resolve the difficult problem of measuring subjective uncertainty within a trust relationship. This paper studies trust propagation and trust network evaluation in social networks by using uncertainty theory. First, we identify types of relationships between decision-makers (DMs) and construct the underlying trust network by defining the correlation function based on uncertain distances. Second, uncertainty optimization models of single-path and comprehensive indirect trust are developed so that the comprehensive indirect trust value between DMs can be simply calculated. A maximum belief degree model is introduced to compute the maximum belief degree and to obtain the optimal trust propagation path between two DMs. Third, by defining such a concept as consilience degree of a trust network, the trust relationship between DMs can be effectively measured. We also evaluate a trust network respectively from the perspectives of individual influence, the consilience level of the decision group and the stability of the local trust network. Finally, a real-world case of selecting the members of an enterprise credit group is illustrated to confirm the validity of our proposed methods and concepts in this paper. (C) 2021 Elsevier B.V. All rights reserved.