Abstract
Link prediction refers to the process of mining and determining whether a link between two nodes in a given network may emerge in the future or it is already present but hidden in the network. Link prediction may be categorized under the class of recommendation systems, e.g., finding or predicting link/recommendation between users and items. Thus, efficient link prediction in social networks is the focus of the study described in this paper. Finding hidden links and extracting missing information in a network will aid in identifying a set of new interactions. We developed a technique for link prediction by exposing the benefits of social network analysis tools and algorithms. We used popular network models commonly used by the research community for testing our algorithm accuracy against well-known algorithms leading to similarity measures. We also decided on using a graph database to model the network for providing better scalability and efficiency compared to storing graph information in a relational database. The experimental results reported in this paper demonstrate how the proposed algorithm outperforms traditional link prediction algorithms described in the literature.
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Afra, S., Aksaç, A., Õzyer, T., Alhajj, R. (2017). Link Prediction by Network Analysis. In: Kawash, J., Agarwal, N., Özyer, T. (eds) Prediction and Inference from Social Networks and Social Media. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-51049-1_5
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