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Mining author relationship in scholarly networks based on tripartite citation analysis

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  • Feifei Wang
  • Xiaohan Wang
  • Siluo Yang

Abstract

Following scholars in Scientometrics as examples, we develop five author relationship networks, namely, co-authorship, author co-citation (AC), author bibliographic coupling (ABC), author direct citation (ADC), and author keyword coupling (AKC). The time frame of data sets is divided into two periods: before 2011 (i.e., T1) and after 2011 (i.e., T2). Through quadratic assignment procedure analysis, we found that some authors have ABC or AC relationships (i.e., potential communication relationship, PCR) but do not have actual collaborations or direct citations (i.e., actual communication relationship, ACR) among them. In addition, we noticed that PCR and AKC are highly correlated and that the old PCR and the new ACR are correlated and consistent. Such facts indicate that PCR tends to produce academic exchanges based on similar themes, and ABC bears more advantages in predicting potential relations. Based on tripartite citation analysis, including AC, ABC, and ADC, we also present an author-relation mining process. Such process can be used to detect deep and potential author relationships. We analyze the prediction capacity by comparing between the T1 and T2 periods, which demonstrate that relation mining can be complementary in identifying authors based on similar themes and discovering more potential collaborations and academic communities.

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  • Feifei Wang & Xiaohan Wang & Siluo Yang, 2017. "Mining author relationship in scholarly networks based on tripartite citation analysis," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-22, November.
    • Handle: RePEc:plo:pone00:0187653
    DOI: 10.1371/journal.pone.0187653
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    References listed on IDEAS

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    1. Raf Guns & Ronald Rousseau, 2014. "Recommending research collaborations using link prediction and random forest classifiers," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1461-1473, November.
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