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 decisi...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Elsevier B.V.
2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/98209/ http://dx.doi.org/10.1016/j.knosys.2021.107610 |
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| Summary: | 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. |
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