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Link prediction is one of the most essential and crucial tasks in complex network research since it seeks to forecast missing links in a network based on current ones. This problem has applications in a variety of scientific disciplines, including social network research, recommendation systems, and biological networks. In previous work, link prediction has been solved through different methods such as path, social theory, topology, and similarity-based. The main issue is that path-based methods ignore topological features, while structure-based methods also fail to combine the path and structured-based features. As a result, a new technique based on the shortest path and topological features' has been developed. The method uses both local and global similarity indices to measure the similarity. Extensive experiments on real-world datasets from a variety of domains are utilized to empirically test and compare the proposed framework to many state-of-the-art prediction techniques. Over 100 iterations, the collected data showed that the proposed method improved on the other methods in terms of accuracy. SI and AA, among the existing state-of-the-art algorithms, fared best with an AUC value of 82%, while the proposed method has an AUC value of 84%. [ABSTRACT FROM AUTHOR]

Personal information management enables users to manage and classify information via the social tagging. The personal information management platform has recently successfully adopted social networks, enabling users to conveniently share their preferences of information with each other. The emerging social networks generate new concepts for designing modern recommender systems in personal information management and sharing platforms. To design a recommender mechanism for the personal information management and sharing platforms, this work incorporates tag-based personalized interest and social network relationships into a modified Bayesian probability model. The proposed system is demonstrated with experimental datasets obtained from a popular social resource sharing website. The performances of the proposed system are evaluated based on the word2vec word embedding model. Experimental results indicate that incorporating social network information and personalized tag-based preference with the Bayesian model can improve the recommendation quality for social information sharing websites. [ABSTRACT FROM AUTHOR]