As mobile social networks (MSNs) are booming and gaining tremendous popularity, there have been an increasing number of communications and interactions among users. Taking this advantage, users in MSNs make decisions via collecting and combining trust information from different users. Hence, trust evaluation technology has become a key requirement for network security in MSNs. In such MSNs, however, the community/group structures are dynamically changing, and users may belong to multiple communities/groups. Therefore, trust evaluation plays a critical role in inferring trustworthy contacts among users. In this paper, an innovative trust inference model is proposed for MSNs, in which multiple dimensional trust metrics are incorporated to reflect the complexity of trust. To infer trust relations between users in MSNs with complex communities, we first construct dynamic implicit social behavioral graphs (DynISBG) based on dynamic complex community/group structures and propose an efficient detection algorithm for DynISBG under fuzzy degree kappa. We then present a multi-dimensional fuzzy trust inferring approach that involves four metrics, that is, static attribute trust factor, dynamic behavioral trust factor, long-term trust evolution factor, and recommendation-based trust opinion. Moreover, to obtain the recommendation-based trust opinion about indirect connected users, we discuss the trust aggregation and propagation along trust path. Finally, we evaluate the performance of our novel approach with simulations. The results show that, compared with the existing approaches, the proposed model provides a more detailed analysis in trust evaluation with higher accuracy. Copyright (C) 2016 John Wiley & Sons, Ltd.