IDEAS home Printed from
   My bibliography  Save this article

Applying centrality measures to impact analysis: A coauthorship network analysis


  • Erjia Yan
  • Ying Ding


Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro‐level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988–2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis.

Suggested Citation

  • Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
  • Handle: RePEc:bla:jamist:v:60:y:2009:i:10:p:2107-2118
    DOI: 10.1002/asi.21128

    Download full text from publisher

    File URL:
    Download Restriction: no


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Gregorio González-Alcaide & Héctor Pinargote & José M. Ramos, 2020. "From cut-points to key players in co-authorship networks: a case study in ventilator-associated pneumonia research," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 707-733, May.
    2. Way-Ren Huang & Chia-Jen Hsieh & Ke-Chiun Chang & Yen-Jo Kiang & Chien-Chung Yuan & Woei-Chyn Chu, 2017. "Network characteristics and patent value—Evidence from the Light-Emitting Diode industry," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-14, August.
    3. Šubelj, Lovro & Fiala, Dalibor & Ciglarič, Tadej & Kronegger, Luka, 2019. "Convexity in scientific collaboration networks," Journal of Informetrics, Elsevier, vol. 13(1), pages 10-31.
    4. Ahn, Sang-Jin, 2020. "Three characteristics of technology competition by IoT-driven digitization," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    5. Cheng Chen & Dan Li & Caixia Man, 2018. "Toward Sustainable Development? A Bibliometric Analysis of PPP-Related Policies in China between 1980 and 2017," Sustainability, MDPI, Open Access Journal, vol. 11(1), pages 1-25, December.
    6. Chakresh Kumar Singh & Shivakumar Jolad, 2019. "Structure and evolution of Indian physics co-authorship networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 385-406, February.
    7. Evren Güney, 2019. "On the optimal solution of budgeted influence maximization problem in social networks," Operational Research, Springer, vol. 19(3), pages 817-831, September.
    8. Claudio Biscaro & Carlo Giupponi, 2014. "Co-Authorship and Bibliographic Coupling Network Effects on Citations," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-12, June.
    9. Zhou, Yuhao & Wang, Ruijie & Zeng, An & Zhang, Yi-Cheng, 2020. "Identifying prize-winning scientists by a competition-aware ranking," Journal of Informetrics, Elsevier, vol. 14(3).
    10. Stuart Oldham & Ben Fulcher & Linden Parkes & Aurina Arnatkevic̆iūtė & Chao Suo & Alex Fornito, 2019. "Consistency and differences between centrality measures across distinct classes of networks," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-23, July.
    11. Chao Lu & Yingyi Zhang & Yong‐Yeol Ahn & Ying Ding & Chenwei Zhang & Dandan Ma, 2020. "Co‐contributorship network and division of labor in individual scientific collaborations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1162-1178, October.
    12. Jong Hwan Suh, 2019. "SocialTERM-Extractor: Identifying and Predicting Social-Problem-Specific Key Noun Terms from a Large Number of Online News Articles Using Text Mining and Machine Learning Techniques," Sustainability, MDPI, Open Access Journal, vol. 11(1), pages 1-44, January.
    13. Shahadat Uddin & Liaquat Hossain & Kim Rasmussen, 2013. "Network Effects on Scientific Collaborations," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-12, February.
    14. Elsa Alvaro & Angel Yanguas-Gil, 2018. "Characterizing the field of Atomic Layer Deposition: Authors, topics, and collaborations," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-19, January.
    15. Kuang-Cheng Chai & Yang Yang & Zhiyong Sui & Ke-Chiun Chang, 2020. "Determinants of highly-cited green patents: The perspective of network characteristics," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-13, October.
    16. Zewen Hu & Angela Lin & Peter Willett, 2019. "Identification of research communities in cited and uncited publications using a co-authorship network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 1-19, January.
    17. Okazaki, Shintaro & Plangger, Kirk & West, Douglas & Menéndez, Héctor D., 2020. "Exploring digital corporate social responsibility communications on Twitter," Journal of Business Research, Elsevier, vol. 117(C), pages 675-682.
    18. Cho, Yung-Jan & Fu, Pei-Wen & Wu, Chi-Cheng, 2017. "Popular Research Topics in Marketing Journals, 1995–2014," Journal of Interactive Marketing, Elsevier, vol. 40(C), pages 52-72.
    19. Qin Zhang & Juneman Abraham & Hui-Zhen Fu, 2020. "Collaboration and its influence on retraction based on retracted publications during 1978–2017," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 213-232, October.
    20. Nasirian, Farzaneh & Mahdavi Pajouh, Foad & Balasundaram, Balabhaskar, 2020. "Detecting a most closeness-central clique in complex networks," European Journal of Operational Research, Elsevier, vol. 283(2), pages 461-475.
    21. Jianhua Hou & Xiucai Yang & Chaomei Chen, 2020. "Measuring researchers’ potential scholarly impact with structural variations: Four types of researchers in information science (1979–2018)," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-26, June.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jamist:v:60:y:2009:i:10:p:2107-2118. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.