TY - JOUR
T1 - Measuring trust in social networks based on linear uncertainty theory
AU - Gong, Zaiwu
AU - Wang, Hui
AU - Guo, Weiwei
AU - Gong, Zejun
AU - Wei, Guo
N1 - Funding Information:
The work in this article was supported by the National Natural Science Foundation of China ( 71571104 , 71871121 , 71171115 ).
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2020/1
Y1 - 2020/1
N2 - In social networks, trust relationships are the basis for interactions among decision nodes. Trust relationships are subjective and dynamic, and there are only few sample data to measure the strength of these connections. Uncertainty theory is a mathematical system that studies the belief degree of experts and provides a new method for measuring trust in social networks. In this paper, uncertainty theory is applied to the modeling of social networks. For any feature where certain information cannot be directly obtained, the recommended trust is derived based on direct trust values, and the constraints of single-path trust chains are established. To avoid secondary uncertainties caused by subjective weighting while considering multi-node, multi-path chains, two weighted trust aggregation operators are developed to accomplish a multi-trust transitive aggregation model. The belief degrees of the nodes, the trust chains and the whole network are quantified, and a social network trust measurement model based on uncertainty theory is constructed. In the case of a lack of data on the trust chain, a trust threshold constraint is used to calculate the range of the incomplete chain.
AB - In social networks, trust relationships are the basis for interactions among decision nodes. Trust relationships are subjective and dynamic, and there are only few sample data to measure the strength of these connections. Uncertainty theory is a mathematical system that studies the belief degree of experts and provides a new method for measuring trust in social networks. In this paper, uncertainty theory is applied to the modeling of social networks. For any feature where certain information cannot be directly obtained, the recommended trust is derived based on direct trust values, and the constraints of single-path trust chains are established. To avoid secondary uncertainties caused by subjective weighting while considering multi-node, multi-path chains, two weighted trust aggregation operators are developed to accomplish a multi-trust transitive aggregation model. The belief degrees of the nodes, the trust chains and the whole network are quantified, and a social network trust measurement model based on uncertainty theory is constructed. In the case of a lack of data on the trust chain, a trust threshold constraint is used to calculate the range of the incomplete chain.
KW - Group decision making
KW - Social network
KW - Trust measure
KW - Uncertain linear distribution
KW - Uncertainty theory
UR - http://www.scopus.com/inward/record.url?scp=85071402662&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2019.08.055
DO - 10.1016/j.ins.2019.08.055
M3 - Article
AN - SCOPUS:85071402662
SN - 0020-0255
VL - 508
SP - 154
EP - 172
JO - Information Sciences
JF - Information Sciences
ER -