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Link Prediction in Co-authorship Networks Using Scopus Data

  • Erik Medina-AcuñaEmail author
  • Pedro Shiguihara-Juárez
  • Nils Murrugarra-Llerena
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 898)

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.

Keywords

Data mining Machine learning Decision trees Co-authorship network Link prediction Supervised learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Erik Medina-Acuña
    • 1
    Email author
  • Pedro Shiguihara-Juárez
    • 1
  • Nils Murrugarra-Llerena
    • 2
  1. 1.Department of Computer ScienceUniversidad Peruana de Ciencias AplicadasLimaPeru
  2. 2.Department of Computer ScienceUniversity of PittsburghPittsburghUSA

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