Abstract
We propose a model to analyze citation growth and influences of fitness (competitiveness) factors in an evolving citation network. Applying the proposed method to modeling citations to papers and scholars in the InfoVis 2004 data, a benchmark collection about a 31-year history of information visualization, leads to findings consistent with citation distributions in general and observations of the domain in particular. Fitness variables based on prior impacts and the time factor have significant influences on citation outcomes. We find considerably large effect sizes from the fitness modeling, which suggest inevitable bias in citation analysis due to these factors. While raw citation scores offer little insight into the growth of InfoVis, normalization of the scores by influences of time and prior fitness offers a reasonable depiction of the field’s development. The analysis demonstrates the proposed model’s ability to produce results consistent with observed data and to support meaningful comparison of citation scores over time.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
We use papers, articles and publications interchangeably. In the data used for this study, a paper may refer to a research article, a book chapter, or a book.
References
Amaral, L.A.N., Scala, A., Barthélémy, M., Stanley, H.E. (2000a). Classes of small-world networks. Proceedings of the National Academy of Sciences of the United States of America, 97(21), 11149–11152. http://www.pnas.org/content/97/21/11149.abstract, http://www.pnas.org/content/97/21/11149.full.pdf+html.
Amaral, L.A.N., Scala, A., Barthelemydagger, M., Stanley, H.E. (2000b). Classes of small-world networks. Proceedings of the National Academy of Science, 97, 11149–11152.
Barabási, A.L. (2009). Scale-free networks: A decade and beyond. Science, 325, 412–413. http://www.sciencemag.org/cgi/content/full/325/5939/412.
Barabasi, A.L., Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.
Bianconi, G., Barabási, A.L. (2001). Competition and multiscaling in evolving networks. Europhysics Letters, 54, 436–442.
Börner, K., Dall’Asta, L., Ke, W., Vespignani, A. (2005). Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams. Complexity, Special Issue on Understanding Complex Systems, 10(4), 58–67.
Burrell, Q.L. (2002). Modelling citation age data: Simple graphical methods from reliability theory. Scientometrics, 55(2), 273–285.
Cronin, B., Overfelt, K. (1994). Citation-based auditing of academic performance. Journal of the American Society for Information Science, 45(2), 61–72.
Fekete, J.D., Grinstein, G., Plaisant, C. (2004). InfoVis 2004 contest: The history of InfoVis. http://www.cs.umd.edu/hcil/iv04contest/.
Fu, L.D., Aliferis, C.F. (2010). Using content-based and bibliometric features for machine learing models to predict citation counts in the biomedical literature. Scientometrics, 85, 257–270. doi:10.1007/s11192-010-0160-5.
Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178, 471–479.
Glanzel, W., Schubert, A. (1993). A characterization of scientometric distributions based on harmonic means. Scientometrics, 26(1), 81–96.
Gorraiz, J., Reimann, R., Gumpenberger, C. (2012). Key factors and considerations in the assessment of international collaboration: A case study for austria and six countries. Scientometrics, 91, 417–433. doi:10.1007/s11192-011-0579-3. http://dx.doi.org/10.1007/s11192-011-0579-3.
Gupta, B.M. (1997). Analysis of distribution of the age of citations in theoretical population genetics. Scientometrics, 40(1), 139–162. doi:10.1007/BF02459265.
Ke, W., Börner, K., Viswanath, L. (2004). Major information visualization authors, papers and topics in the acm library. In INFOVIS ’04: Proceedings of the IEEE Symposium on Information Visualization (INFOVIS’04). Washington, DC: IEEE Computer Society.
Lee, S., Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702. doi:10.1177/0306312705052359, http://sss.sagepub.com/content/35/5/673.abstract, http://sss.sagepub.com/content/35/5/673.full.pdf+html.
Lindsey, D. (1980). Production and citation measures in the sociology of science: The problem of multiple authorship. Social Studies of Science, 10(2), 145–162.
Nicolaisen, J. (2007). Citation analysis. Annual Review of Information Science and Technology, 43, 609–641.
Perc, M. (2010). Growth and structure of slovenia’s scientific collaboration network. Journal of Informetrics, 4(4), 475–482. doi:10.1016/j.joi.2010.04.003, http://www.sciencedirect.com/science/article/pii/S1751157710000362.
Peters, H.P.F., van Raan, A.F.J. (1994). On determinants of citation scores: A case study in chemical engineering. Journal of the American Society for Information Science, 45(1), 39–49.
Plaisant, C., Fekete, J., Grinstein, G. (2004). Promoting insight based evaluation of visualizations: From contest to benchmark repository. Technical report, HCIL, University of Maryland. http://www.cs.umd.edu/hcil/pubs/tech-reports.shtml.
Price, D.J. (1976). A general theory of bibliometric and other cumulative advantage processes. Journal of the American Society for Information Science, 27(5-6), 292–306.
Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25, 348–349.
Redner, S. (1998). How popular is your paper? an empirical study of the citation distribution. The European Physical Journal B—Condensed Matter and Complex Systems, 4(2), 131–134.
Robert, G., Sumner, J. (1995). Utilizing the age of references to control the exhaustivity of the reference representation in information retrieval. Information Processing & Management, 31, 29–45.
Sikorav, J. (1991). The utility of scientific papers. Scientometrics, 21, 49–68. doi:10.1007/BF02019182. http://dx.doi.org/10.1007/BF02019182.
Zhu, H., Wang, X., Zhu, J.Y. (2003). Effect of aging on network structure. Physics Review E, 68(5), 056121. doi:10.1103/PhysRevE.68.056121.
Zyczkowski, K. (2009). Citation graph, weighted impact factors and performance indices. Scientometrics, 85, 301–315. doi:10.1007/s11192-010-0208-6, http://arxiv.org/abs/0904.2110.
Acknowledgments
I would like to thank Thomas Carsey, Paul Solomon, and Cassidy R. Sugimoto for valuable discussions. I also appreciate constructive comments from Jeff Harden, Ellen Gutman and anonymous Scientometrics reviewers.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ke, W. A fitness model for scholarly impact analysis. Scientometrics 94, 981–998 (2013). https://doi.org/10.1007/s11192-012-0787-5
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-012-0787-5