Skip to main content
Log in

A measure of authors’ centrality in co-authorship networks based on the distribution of collaborative relationships

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Although there are many measures of centrality of individuals in social networks, and those centrality measures can be applied to the analysis of authors’ importance in co-authorship networks, the distribution of an author’s collaborative relationships among different communities has not been considered. This distribution or extensity is an important aspect of authors’ activity. In the present study, we will propose a new measure termed extensity centrality, taking into account the distribution of an author’s collaborative relationships. In computing the strength of collaborative ties, which is closely related to the extensity centrality, we choose Salton’s measure. We choose the ACM SIGKDD data as our testing data set, and analyze the result of authors’ importance from different points of view.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bonacich, P. (1972), Factoring and weighting approaches to status scores and clique identification, Journal of Mathematical Sociology, 2: 113–20.

    Google Scholar 

  • Bonacich, P. (1987), Power and centrality: A family of measures, The American Journal of Sociology, 92: 1170–1182.

    Article  Google Scholar 

  • Borgatti, S., Everett, M., Shirey, P. (1990), LS sets, Lambda sets and other cohesive subsets, Social Networks, 12: 337–357.

    Article  MathSciNet  Google Scholar 

  • Borgatti, S., Everett, M., Freeman, L. (2002), UCINET for Windows: Software for Social Network Analysis, Analytic Technologies, Harvard, MA.

    Google Scholar 

  • Borner, K., Dall’asta, L., Ke, W., Vespignani, A. (2005), Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams, Complexity, 10: 57–67.

    Article  Google Scholar 

  • Braun, T., Glänzel, W., Schubert, R. (2001), Publication and cooperation patterns of the authors of neuroscience journals, Scientometrics, 51: 499–510.

    Article  Google Scholar 

  • Brin, S., Page, L. (1998), The anatomy of a large-scale hypertextual Web search engine, Proceedings of 7th International World Wide Web Conference, 101–117.

  • Chakrabarti, D., Faloutsos, C. (2006), Graph mining: Laws, generators, and algorithms, ACM Computing Surveys, 38: 1–69.

    Article  Google Scholar 

  • Freeman, L. C. (1979), Centrality in networks: I. conceptual clarification, Social Networks, 1: 215–239.

    Article  Google Scholar 

  • Genest, C., Thibault, C. (2001), Investigating the concentration within a research community using joint publications and co-authorship via intermediaries, Scientometrics, 51: 429–440.

    Article  Google Scholar 

  • Getoor, L., Diehl, C. (2005), Link mining: A survey, ACM SIGKDD Explorations Newsletter, 7: 3–12.

    Article  Google Scholar 

  • Hou, H., Kretschmer, H., Liu, Z. (2008), The structure of scientific collaboration networks in Scientometrics, Scientometrics, 75: 189–202.

    Article  Google Scholar 

  • Kretschmer, H. (2004), Author productivity and geodesic distance in bibliographic co-authorship networks, and visibility on the Web, Scientometrics, 60: 409–420.

    Article  Google Scholar 

  • Kleinberg, J. (1999), Authoritative sources in a hyperlinked environment, Journal of the ACM, 46: 604–632.

    Article  MATH  MathSciNet  Google Scholar 

  • Liu, X., Bollen, J., Nelson, M., Sompel, H. (2005), Co-authorship networks in the digital library research community, Information Processing and Management, 41: 1462–1480.

    Article  Google Scholar 

  • Newman, M. (2001a), Scientific collaboration networks. I. Network construction and fundamental results, Physical Review E, 64.

  • Newman, M. (2001b), Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality, Physical Review E, 64.

  • Newman, M. (2003), The structure and function of complex networks, SIAM Review, 45: 167–256.

    Article  MATH  MathSciNet  Google Scholar 

  • Salton, G., Mcgill, M. (1983), Introduction to Modern Information Retrieval, McGraw-Hill, N.Y.

    MATH  Google Scholar 

  • Tutzauer, F. (2007), Entropy as a measure of centrality in networks characterized by path-transfer flow, Social Networks, 29: 249–265.

    Article  Google Scholar 

  • Wasserman, S., Faust., K. (1994), Social Network Analysis. Methods and Applications, Cambridge University Press, Cambridge.

    Google Scholar 

  • Yin, L., Kretschmer, H., Hanneman, R., Liu, Z. (2006), Connection and stratification in research collaboration: An analysis of the COLLNET network, Information Processing and Management, 42: 1599–1613.

    Article  Google Scholar 

  • Yoshikane, F., Nozawa, T., Tsuji, K. (2006), Comparative analysis of co-authorship networks considering authors’ roles in collaboration: Differences between the theoretical and application areas, Scientometrics, 68: 643–655.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haiyang Lu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lu, H., Feng, Y. A measure of authors’ centrality in co-authorship networks based on the distribution of collaborative relationships. Scientometrics 81, 499–511 (2009). https://doi.org/10.1007/s11192-008-2173-x

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-008-2173-x

Keywords

Navigation