Exploiting similarities of user friendship networks across social networks for user identification

Abstract User identification has been attracting considerable attention from academia. Due to the uniqueness and difficulty of faking friendship networks, some friendship-based methods have been presented to improve the identification performance. However, the information redundancies in k-hop (k >  1) neighbors and their contributions to user identification have not been fully analyzed in the existing work. Addressing these two issues helps to understand the problem of friendship-based user identification and to propose more effective solutions. In this paper, we first obtain ground-truth friendship networks across three popular social sites; then, we analyze the similarities of k -hop neighbors to fully characterize the information redundancies in the friendship network. We apply these information redundancies in several classifiers to study their contributions to user identification. Furthermore, we apply the friendship-based information redundancies jointly with the display-name-based information redundancies to perform user identification. The experiments show that (1) the similarities related to the 1-hop neighbors contribute to user identification much more than do the other similarities; (2) the information redundancies in the k-hop (k >  1) neighbors are also very useful for user identification; and (3) jointly applying display-name-based information redundancies can provide better performance and improve the universality of the identification method.