Social-IFD: Personalized Influential Friends Discovery Based on Semantics in LBSN

Social influence is a hot topic in social network research, and this paper focuses on how to search for the most influential friends for a target user. The key point is to measure the influence between different users, such as adjacent users and the non-adjacent users. However, the traditional method, called the IS model, can only calculate the influence strength between neighboring users based on the inner-product of the Influence vector and the Susceptibility vector. In this paper, the social-IFD algorithm is proposed to compute the influence between different users (not only neighboring users but also non-adjacent users) based on network structure and the semantic information of users in LBSN, which has promoted the development of the IS model. Furthermore, we propose social-IFD ++ algorithm based on dynamic program to reduce the complexity of the social-IFD algorithm. Experiment results on two real large-scale network show that the average precision of the proposed social-IFD algorithm is 30.5% higher than the average precision of the IS model. In addition, the CPU running time of the proposed social-IFD ++ algorithm is nearly ten times lower than that of the IS model. It indicates that the proposed two algorithms have superior performance.