Abstract
In recent years, several national and community-driven conference rankings have been compiled. These rankings are often taken as indicators of reputation and used for a variety of purposes, such as evaluating the performance of academic institutions and individual scientists, or selecting target conferences for paper submissions. Current rankings are based on a combination of objective criteria and subjective opinions that are collated and reviewed through largely manual processes. In this setting, the aim of this paper is to shed light into the following question: to what extent existing conference rankings reflect objective criteria, specifically submission and acceptance statistics and bibliometric indicators? The paper specifically considers three conference rankings in the field of Computer Science: an Australian national ranking, a Brazilian national ranking and an informal community-built ranking. It is found that in all cases bibliometric indicators are the most important determinants of rank. It is also found that in all rankings, top-tier conferences can be identified with relatively high accuracy through acceptance rates and bibliometric indicators. On the other hand, acceptance rates and bibliometric indicators fail to discriminate between mid-tier and bottom-tier conferences.






Similar content being viewed by others
Notes
Subsequently, tiers A+ and A were merged and tier L was removed.
Multiple versions of this ranking are circulating in the Web while its real origin and the ranking methodology is unknown.
Note that a slightly different taxonomy for conference categorization into subdisciplines is used than in Microsoft Academic Search.
References
Batista, G. E., Prati, R. C., & M. C. Monard. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20–29.
Breiman, L., Friedman, J. H., Olshen, R. A., & C. J. Stone. (1984). Classification and Regression Trees, Wadsworth.
Chen, P., Xie, H., Maslov, S. & S. Redner. (2007). Finding scientific gems with Google’s PageRank algorithm. Journal of Informetrics, 1(1), 8–15.
Eckmann, M., Rocha, A., & J. Wainer. (2012). Relationship between high-quality journals and conferences in computer vision. Scientometrics, 90(2), 617–630.
Egghe, L. (2006). Theory and practice of the g-index. Scientometrics, 69(1):131–152.
Garfield, E. (1972). Citation analysis as a tool in journal evaluation, American Association for the Advancement of Science.
Goodrum, A., McCain, K. W., Lawrence, S., & C.L. Giles. (2001). Scholarly publishing in the internet age: a citation analysis of computer science literature. Inf. Process. Manage, 37(5), 661–675.
Hamermesh, D. S., Pfann, (2000) G. A. Markets for reputation: evidence on quality and quantity in academe, SSRN eLibrary. http://ssrn.com/paper=1533208.
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.
Jacso, P. (2011). The pros and cons of Microsoft Academic Search from a bibliometric perspective. Online Information Review, 35(6), 983–997.
Jensen, P., Rouquier, J. B., & Y. Croissant. (2009). Testing bibliometric indicators by their prediction of scientists promotions. Scientometrics, 78(3), 467–479.
Laender, A. H. F., de Lucena, C. J. P., Maldonado, J. C., de Souza e Silva, E., & N. Ziviani. (2008). Assessing the research and education quality of the top brazilian computer science graduate programs. SIGCSE Bull., 40, 135–145.
Laloë, F., & R. Mosseri. (2009). Bibliometric evaluation of individual researchers: not even right... not even wrong!. Europhysics News, 40(5), 26–29.
Liu, X., Bollen, J., Nelson, M.L., & H. Van de Sompel. (2005). Co-authorship networks in the digital library research community. Information Processing & Management, 41(6), 1462–1480.
Ma, N., Guan, J., & Y. Zhao. (2008). Bringing PageRank to the citation analysis. Information Processing & Management, 44(2), 800–810.
Moed, H. F., & M. S. Visser. (2007). Developing bibliometric indicators of research performance in computer science: An exploratory study, Tech. Rep. CWTS Report 2007-01, Centre for Science and Technology Studies (CWTS), Leiden University, the Netherlands. http://www.cwts.nl/pdf/NWO_Inf_Final_Report_V_210207.pdf.
Page, L., Brin, S., Motwani, R. & T. Winograd. (1998). The PageRank citation ranking: bringing order to the web, Tech. rep., Stanford Digital Library Technologies Project.
Rahm, E., & A. Thor. (2005). Citation analysis of database publications. ACM Sigmod Record, 34(4), 48–53.
Sakr, S., & M. Alomari. (2012). A decade of database conferences: a look inside the program committees. Scientometrics, 91(1), 173–184.
Shi, X., Tseng, B., & L. Adamic (2009). Information diffusion in computer science citation networks. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2009).
Shi, X., Leskovec, J., & D. A. McFarland. (2010). Citing for high impact. In: Proceedings of the 10th Annual Joint Conference on Digital Libraries, ACM, pp. 49–58.
Sidiropoulos, A., & Y. Manolopoulos. (2005). A new perspective to automatically rank scientific conferences using digital libraries. Information Processing & Management, 41(2), 289–312.
Silva Martins, W., Gonçalves, M. A., Laender, A. H. F., & G. L. Pappa. (2009). Learning to assess the quality of scientific conferences: a case study in computer science. In: Proceedings of the Joint International Conference on Digital Libraries (JCDL), Austin, pp. 193–202.
Silva Martins, W., Gonçalves, M. A., Laender, A. H. F., & N. Ziviani. (2010). Assessing the quality of scientific conferences based on bibliographic citations. Scientometrics, 83(1), 133–155.
Vanclay, J. K. (2011). An evaluation of the australian research council’s journal ranking. Journal of Informetrics, 5(2), 265–274.
Zhou, D., Orshanskiy, S. A., Zha, H., & C. L. Giles. (2008) Co-ranking authors and documents in a heterogeneous network. In: Seventh IEEE International Conference on Data Mining, ICDM 2007, IEEE, pp. 739–744.
Zhuang, Z., Elmacioglu, E., Lee, D., & C. L. Giles. (2007). Measuring conference quality by mining program committee characteristics. In: Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, ACM, pp. 225–234.
Acknowledgments
The authors thank Luciano García-Bañuelos, Marju Valge, Svetlana Vorotnikova and Karina Kisselite for their input during the initial phase of this work. This work was partially funded by the EU FP7 project LiquidPublication (FET-Open grant number 213360).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Küngas, P., Karus, S., Vakulenko, S. et al. Reverse-engineering conference rankings: what does it take to make a reputable conference?. Scientometrics 96, 651–665 (2013). https://doi.org/10.1007/s11192-012-0938-8
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-012-0938-8
Keywords
- Conference rankings
- Computer science
- Bibliometrics
- Conference acceptance rate
- Publication counts
- Citation counts
- Objective criteria