, Volume 96, Issue 1, pp 1–9 | Cite as

Experimenting with the partnership ability φ-index on a million computer scientists

  • Guillaume CabanacEmail author


Schubert introduced the partnership ability φ-index relying on a researcher’s number of co-authors and collaboration rate. As a Hirsch-type index, φ was expected to be consistent with Schubert–Glänzel’s model of h-index. Schubert demonstrated this relationship with the 34 awardees of the Hevesy medal in the field of nuclear and radiochemistry (r 2 = 0.8484). In this paper, we upscale this study by testing the φ-index on a million researchers in computer science. We found that the Schubert–Glänzel’s model correlates with the million empirical φ values (r 2 = 0.8695). In addition, machine learning through symbolic regression produces models whose accuracy does not exceed a 6.1 % gain (r 2 = 0.9227). These results suggest that the Schubert–Glänzel’s model of φ-index is accurate and robust on the domain-wide bibliographic dataset of computer science.


Partnership ability index Co-authorship Empirical validation Symbolic regression 


  1. Alonso, S., Cabrerizo, F., Herrera-Viedma, E., Herrera, F. (2009). h-Index: a review focused in its variants, computation and standardization for different scientific fields. Journal of Informetrics, 3(4), 273–289. doi: 10.1016/j.joi.2009.04.001.CrossRefGoogle Scholar
  2. Bar-Ilan, J. (2008). Informetrics at the beginning of the 21st century: a review. Journal of Informetrics, 2(1), 1–52. doi: 10.1016/j.joi.2007.11.001.CrossRefGoogle Scholar
  3. Cabanac, G. (2011). Accuracy of inter-researcher similarity measures based on topical and social clues. Scientometrics, 87(3), 597–620. doi: 10.1007/s11192-011-0358-1.CrossRefGoogle Scholar
  4. Cabanac, G. (2012). Shaping the landscape of research in information systems from the perspective of editorial boards: a scientometric study of 77 leading journals. Journal of the American Society for Information Science and Technology, 63(5), 977–996. doi: 10.1002/asi.22609.CrossRefGoogle Scholar
  5. Chen, J., & Konstan, J. A. (2010). Conference paper selectivity and impact. Communications of the ACM, 53(6), 79–83. doi: 10.1145/1743546.1743569.
  6. Elmacioglu, E., & Lee, D. (2005). On six degrees of separation in DBLP-DB and more. SIGMOD Record, 34(2), 33–40. doi: 10.1145/1083784.1083791.CrossRefGoogle Scholar
  7. Freyne, J., Coyle, L., Smyth, B., & Cunningham, P. (2010). Relative status of journal and conference publications in Computer Science. Communications of the ACM, 53(11), 124–132. doi: 10.1145/1839676.1839701.CrossRefGoogle Scholar
  8. Glänzel, W. (2006). On the h-index: a mathematical approach to a new measure of publication activity and citation impact. Scientometrics, 67(2), 315–321. doi: 10.1007/s11192-006-0102-4.CrossRefGoogle Scholar
  9. Hirsch, JE. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the USA, 102(46), 16569–16572. doi: 10.1073/pnas.0507655102.
  10. Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. Cambridge: MIT Press.zbMATHGoogle Scholar
  11. Ley, M. (2002). The DBLP computer science bibliography: evolution, research issues, perspectives. In: A. H. F. Laender, A. L. Oliveira (eds.) SPIRE’02 : Proceedings of the 9th international conference on string processing and information retrieval (vol. 2476, pp. 1–10). Springer, LNCS. doi: 10.1007/3-540-45735-6_1.
  12. Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–324.Google Scholar
  13. Mallig, N. (2010). A relational database for bibliometric analysis. Journal of Informetrics, 4(4), 564–580. doi: 10.1016/j.joi.2010.06.007.CrossRefGoogle Scholar
  14. Rousseau, R. (2012). Comments on “A Hirsch-type index of co-author partnership ability”. Scientometrics, 91(1), 309–310. doi: 10.1007/s11192-011-0606-4.CrossRefGoogle Scholar
  15. Schmidt, M., Lipson, H. (2009). Distilling free-form natural laws from experimental data. Science, 324(5923), 81–85. doi: 10.1126/science.1165893.CrossRefGoogle Scholar
  16. Schreiber, M., Malesios, C., Psarakis, S. (2012). Exploratory factor analysis for the Hirsch index, 17 h-type variants, and some traditional bibliometric indicators. Journal of Informetrics, 6(3), 347–358. doi: 10.1016/j.joi.2012.02.001.CrossRefGoogle Scholar
  17. Schubert, A. (2012a). A Hirsch-type index of co-author partnership ability. Scientometrics, 91(1), 303–308. doi: 10.1007/s11192-011-0559-7.CrossRefGoogle Scholar
  18. Schubert, A. (2012b). Jazz discometrics: a network approach. Journal of Informetrics, 6(4), 480–484. doi: 10.1016/j.joi.2012.04.004.CrossRefGoogle Scholar
  19. Schubert, A., Glänzel, W. (2007) A systematic analysis of Hirsch-type indices for journals. Journal of Informetrics, 1(3), 179–184. doi: 10.1016/j.joi.2006.12.002.CrossRefGoogle Scholar
  20. Wolfram, D. (2006). Applications of SQL for informetric frequency distribution processing. Scientometrics, 67(2), 301–313. doi: 10.1007/s11192-006-0101-5.CrossRefGoogle Scholar
  21. Zhao, S. X., Rousseau, R., Ye, F. Y. (2011). h-Degree as a basic measure in weighted networks. Journal of Informetrics, 5(4), 668–677. doi: 10.1016/j.joi.2011.06.005.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2012

Authors and Affiliations

  1. 1.Computer Science Department, IRIT UMR 5505 CNRSUniversity of ToulouseToulouse Cedex 9France

Personalised recommendations