Abstract
Link Prediction is a common task for social networks and recommendation systems. In this paper, we study the problem of link prediction on Scopus co-authorship networks. We used many well-known relational features, and evaluate them with five different classifiers. Finally, we perform a feature analysis to determine the most crucial features in this setup.
The dataset was generated with Elsevier API http://api.elsevier.com and Scopus http://www.scopus.com.
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Notes
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A Sybil is a user, usually fake, that takes advantage of a system to make negative actions such as steal personal information.
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Medina-Acuña, E., Shiguihara-Juárez, P., Murrugarra-Llerena, N. (2019). Link Prediction in Co-authorship Networks Using Scopus Data. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_10
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DOI: https://doi.org/10.1007/978-3-030-11680-4_10
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