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Predicting Co-Author Relationship in Medical Co-Authorship Networks

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  • Qi Yu
  • Chao Long
  • Yanhua Lv
  • Hongfang Shao
  • Peifeng He
  • Zhiguang Duan

Abstract

Research collaborations are encouraged because a synergistic effect yielding good results often appears. However, creating and organizing a strong research group is a difficult task. One of the greatest concerns of an individual researcher is locating potential collaborators whose expertise complement his best. In this paper, we propose a method that makes link predictions in co-authorship networks, where topological features between authors such as Adamic/Adar, Common Neighbors, Jaccard's Coefficient, Preferential Attachment, Katzβ, and PropFlow may be good indicators of their future collaborations. Firstly, these topological features were systematically extracted from the network. Then, supervised models were used to learn the best weights associated with different topological features in deciding co-author relationships. Finally, we tested our models on the co-authorship networks in the research field of Coronary Artery Disease and obtained encouraging accuracy (the precision, recall, F1 score and AUC were, respectively, 0.696, 0.677, 0.671 and 0.742 for Logistic Regression, and respectively, 0.697, 0.678, 0.671 and 0.743 for SVM). This suggests that our models could be used to build and manage strong research groups.

Suggested Citation

  • Qi Yu & Chao Long & Yanhua Lv & Hongfang Shao & Peifeng He & Zhiguang Duan, 2014. "Predicting Co-Author Relationship in Medical Co-Authorship Networks," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-7, July.
  • Handle: RePEc:plo:pone00:0101214
    DOI: 10.1371/journal.pone.0101214
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    References listed on IDEAS

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    1. Katz, J. Sylvan & Martin, Ben R., 1997. "What is research collaboration?," Research Policy, Elsevier, vol. 26(1), pages 1-18, March.
    2. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
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    Cited by:

    1. Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.

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