Multi-dimensional fuzzy trust evaluation for mobile social networks based on dynamic community structures

摘要

As mobile social networks (MSNs) are booming and gaining tremendous popularity, there have been anrnincreasing number of communications and interactions among users. Taking this advantage, users in MSNsrnmake decisions via collecting and combining trust information from different users. Hence, trust evaluationrntechnology has become a key requirement for network security in MSNs. In such MSNs, however,rnthe community/group structures are dynamically changing, and users may belong to multiple communities/rngroups. Therefore, trust evaluation plays a critical role in inferring trustworthy contacts among users. Inrnthis paper, an innovative trust inference model is proposed for MSNs, in which multiple dimensional trustrnmetrics are incorporated to reflect the complexity of trust. To infer trust relations between users in MSNsrnwith complex communities, we first construct dynamic implicit social behavioral graphs (DynISBG) basedrnon dynamic complex community/group structures and propose an efficient detection algorithm for DynISBGrnunder fuzzy degree u0002. We then present a multi-dimensional fuzzy trust inferring approach that involves fourrnmetrics, that is, static attribute trust factor, dynamic behavioral trust factor, long-term trust evolution factor,rnand recommendation-based trust opinion. Moreover, to obtain the recommendation-based trust opinionrnabout indirect connected users, we discuss the trust aggregation and propagation along trust path. Finally, wernevaluate the performance of our novel approach with simulations. The results show that, compared with thernexisting approaches, the proposed model provides a more detailed analysis in trust evaluation with higherrnaccuracy.