Locality-Sensitive Hashing-Based Link Prediction Process on Smart Campus Education or Online Social Platform

Abstract

With the development of the Internet, smart campus education and online social platforms have become the mainstream of establishing social relationships. Although many users communicate by social networks, the social networks are caught in the problem of relationship sparsity, which severely impedes users’ communication space. More fatally, in the course of building social relationships, the disclosure of sensitive information will cause users’ privacy vulnerable to be compromised by attackers. Therefore, this paper proposes a potential social relationships prediction approach based on locality-sensitive hashing (LSH) to address the above issues. Specifically, the LSH clusters similar users into the same bucket, and the fuzzy computing method is developed to predict the types of social relationships among these similar users. To further alleviate the relationship sparsity problem, the existing social network structure is utilized to predict users’ social relationships and relationship types. Furthermore, the rationality of prediction results is verified by using the social balance theory (SBT). Finally, massive experiments are executed on Epinions, and the experimental results further confirmed the efficiency and accuracy of our methodology in terms of link prediction while guaranteeing privacy-preservation.