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)


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.


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


  1. 1.
    Adamic, L.A., Adar, E.: Friends and neighbors on the Web. Soc. Netw. 25, 211–230 (2003)CrossRefGoogle Scholar
  2. 2.
    Barabási, A.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Daud, A., Ahmad, M., Malik, M.S.I., Che, D.: Using machine learning techniques for rising star prediction in co-author network. Scientometrics 102, 1687–1711 (2014)CrossRefGoogle Scholar
  4. 4.
    Esslimani, I., Brun, A., Boyer, A.: Densifying a behavioral recommender system by social networks link prediction methods. Soc. Netw. Anal. Min. 1, 159–172 (2010)CrossRefGoogle Scholar
  5. 5.
    Gong, N.Z., Frank, M., Mittal, P.: SybilBelief: a semi-supervised learning approach for structure-based sybil detection. IEEE Trans. Inf. Forensics Secur. 9, 976–987 (2014)CrossRefGoogle Scholar
  6. 6.
    Jaccard, P.: Etude comparative de la distribution florale dans une portion des Alpes et du Jura (1901)Google Scholar
  7. 7.
    Julian, K., Lu, W.: Application of machine learning to link prediction (2016)Google Scholar
  8. 8.
    Llerena, N.E.M., Berton, L., Lopes, A.D.A.: Graph-based cross-validated committees ensembles. In: 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN) (2012)Google Scholar
  9. 9.
    Pujari, M., Kanawati, R.: Link prediction in multiplex networks. Netw. Heterog. Media 10, 17–35 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Schall, D.: Link prediction for directed graphs. Soc. Netw.-Based Recomm. Syst. 7–31 (2015)Google Scholar
  11. 11.
    Singh, H., Tomar, D., Agarwal, S.: Link prediction for authorship association in heterogeneous network using streaming classification. Int. J. Grid Distrib. Comput. 9, 135–150 (2016)Google Scholar
  12. 12.
    Wang, P., Xu, B., Wu, Y., Zhou, X.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58, 1–38 (2014)Google Scholar
  13. 13.
    Yu, C., Zhao, X., An, L., Lin, X.: Similarity-based link prediction in social networks: a path and node combined approach. J. Inf. Sci. 43, 683–695 (2016)CrossRefGoogle Scholar
  14. 14.
    Zhang, J.: Uncovering mechanisms of co-authorship evolution by multirelations-based link prediction. Inf. Process. Manag. 53, 42–51 (2017)CrossRefGoogle Scholar
  15. 15.
    Zhou, X., Ding, L., Li, Z., Wan, R.: Collaborator recommendation in heterogeneous bibliographic networks using random walks. Inf. Retr. J. 20, 317–337 (2017)CrossRefGoogle Scholar

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

Personalised recommendations