Skip to main content
Log in

f-Value: measuring an article’s scientific impact

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

The f-value is a new indicator that measures the importance of a research article by taking into account all citations received, directly and indirectly, up to depth n. The f-value considers all information present in a Citation Graph in order to produce a ranking of the articles. Apart from the mathematical equation that calculates the f-value, we also present the corresponding algorithm with its implementation, plus an experimental comparison of f-value with two known indicators of an article’s scientific importance, namely, the number of citations and the Page Rank for citation analysis. Finally, we discuss the similarities and differences among the indicators.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Anderson, T.R., Hankin, R. K. S., & Killworth P. D. (2008) Beyond the durfee square: Enhancing the h-index to score total publication output. Scientometrics 76(3), 577–588. doi:10.1007/s11192-007-2071-2.

    Article  Google Scholar 

  • Austin, D. (2006). How Google finds your needle in the web’s haystack. http://www.ams.org/featurecolumn/archive/pagerank.html.

  • Boldi, P., Santini, M., & Vigna, S. (2009). Pagerank: Functional dependencies. ACM Transactions on Information Systems 27(4), 1–23. doi:10.1145/1629096.1629097.

    Article  Google Scholar 

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30, 107–117.

    Google Scholar 

  • Citeseer. (1997). http://www.citeseer.ist.psu.edu.

  • Dervos, D., & Kalkanis, T. (2005). cc-IFF: A cascading citations impact factor framework for the automatic rankings of research publications. In 3rd IEEE international workshop on intelligent data acquisition and advanced computer systems: technology and applications (IDAACS 2005), Sofia, Bulgaria.

  • Dervos, D., & Klimis, L. (2008). Exploiting cascading citations for retrieval. In Proceeding of the ASSIST 2008 annual meeting.

  • Dervos, D., Samaras, N., Evangelidis, G., & Folias, T. (2006). A new framework for the citation indexing paradigm. In Proceedings of the ASSIST 2006 annual meeting, Austin, Texas, USA.

  • Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.

    Article  MathSciNet  Google Scholar 

  • Fragkiadaki, E., Evangelidis, G., Samaras, N., & Dervos, D. (2009). Cascading citations indexing framework algorithm implementation and testing. Informatics, Panhellenic conference on Informatics, 70–74. doi:10.1109/PCI.2009.30.

  • Garfield, E. (1955). Citation indexes for science. A new dimension in documentation through association of ideas. Science 122, 1123–1127.

    Article  Google Scholar 

  • Garfield, E. (1999). Journal impact factor: A brief review. CMAJ 161(8), 979–980.

    Google Scholar 

  • Garfield, E. (2005). The agony and the ecstasy—the history and meaning of the journal impact factor. In International Congress on Peer Review And Biomedical Publication.

  • Giles, C. L., Bollacker, K. D., & Lawrence, S. (1998). Citeseer: An automatic citation indexing system (pp. 89–98). New York: ACM Press.

    Google Scholar 

  • Guns, R., & Rousseau, R. (2009). Real and rational variants of the h-index and the g-index. Journal of Informetrics, 3(1): 64–71. doi:10.1016/j.joi.2008.11.004.

    Article  Google Scholar 

  • Hirsch, J. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102, 16569–16572.

    Google Scholar 

  • Jin, B., Liang, L., Rousseau, R., & Egghe, L. (2007). The R- and AR-indices: Complementing the h-index. Chinese Science Bulletin, 52(6), 855–863. doi:10.1007/s11434-007-0145-9.

    Article  Google Scholar 

  • Katsaros, D., Sidiropoulos, A., & Manopoulos, Y. (2007). Age decaying h-index for social network of citations. In SAW proceedings of the BIS 2007 workshop on social aspects of the web, Poznan, Poland, April 27, 2007, CEUR-WS.org. CEUR workshop proceedings. 245.

  • Kräutler, V. (2006). The Google pagerank algorithm in 126 lines of Python. http://www.kraeutler.net/vincent/essays/googlepagerankinpython.

  • Ma, N., Guan, J., & Zhao, Y. (2008). Bringing pagerank to the citation analysis. Information Processing and Management, 44(2): 800–810. doi:10.1016/j.ipm.2007.06.006.

    Article  Google Scholar 

  • Rousseau, R. (1987). The Gozinto theorem: Using citations to determine influences on a scientific publication. Scientometrics, 11(3–4): 217–229.

    Article  Google Scholar 

  • Sidiropoulos, A., Katsaros, D., & Manolopoulos, Y. (2007). Generalized hirsch h-index for disclosing latent facts in citation networks. Scientometrics, 72(2), 253–280 doi:10.1007/s11192-007-1722-z.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eleni Fragkiadaki.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fragkiadaki, E., Evangelidis, G., Samaras, N. et al. f-Value: measuring an article’s scientific impact. Scientometrics 86, 671–686 (2011). https://doi.org/10.1007/s11192-010-0302-9

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-010-0302-9

Keywords

Navigation