Abstract
This paper explores scientific metrics in citation networks in scientific communities, how they differ in ranking papers and authors, and why. In particular we focus on network effects in scientific metrics and explore their meaning and impact. We initially take as example three main metrics that we believe significant; the standard citation count, the more and more popular h-index, and a variation we propose of PageRank applied to papers (called PaperRank) that is appealing as it mirrors proven and successful algorithms for ranking web pages and captures relevant information present in the whole citation network. As part of analyzing them, we develop generally applicable techniques and metrics for qualitatively and quantitatively analyzing such network-based indexes that evaluate content and people, as well as for understanding the causes of their different behaviors. We put the techniques at work on a dataset of over 260K ACM papers, and discovered that the difference in ranking results is indeed very significant (even when restricting to citation-based indexes), with half of the top-ranked papers differing in a typical 20-element long search result page for papers on a given topic, and with the top researcher being ranked differently over half of the times in an average job posting with 100 applicants.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
de Solla Price, D.: Little Science - Big Science. Columbia Univ. Press, New York (1963)
Garfield, E.: Citation Indexing. ISI Press (1979)
Glänzel, W.: Bibliometrics as a research field, A course on theory and application of bibliometric indicators, Magyar Tudományos Akadémia, Course Handouts (2003)
Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences 102(46), 16569–16572 (2005)
Chen, P., Xie, H., Maslov, S., Redner, S.: Finding Scientific Gems with Google. Journal of Informetrics 1(1), 8–15 (2007)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998)
Bianchini, M., Gori, M., Scarselli, F.: Inside PageRank. ACM Transactions on Internet Technology 5(1) (2005)
Del Corso, G.M., Gull, A., Romani, F.: Fast PageRank Computation via a Sparse Linear System. Internet Mathematics 2(3), 251–273 (2005)
Diligenti, M., Marco Gori, M., Maggini, M.: Web Page Scoring Systems for Horizontal and Vertical Search. In: WWW Conference 2002, USA, May 7-11 (2002)
Sun, Y., Giles, L.C.: Popularity Weighted Ranking for Academic Digital Libraries. In: 29th European Conference on Information Retrieval 2007, Rome, Italy, pp. 605–612 (2007)
Bernstam, E.V., Herskovic, J.R., Aphinyanaphongs, Y.: Using Citation Data to Improve Retrieval from MEDLINE. Journal of the American Medical Informatics Association 13(1), 96–105 (2006)
Langville, A.N., Meyer, C.D.: Deeper Inside PageRank. Internet Mathematics 1(3), 335–380 (2004)
Kendall, M., Gibbons, J.D.: Rank Correlation Methods. Edward Arnold, London (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Krapivin, M., Marchese, M., Casati, F. (2009). Exploring and Understanding Scientific Metrics in Citation Networks. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02469-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-02469-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02468-9
Online ISBN: 978-3-642-02469-6
eBook Packages: Computer ScienceComputer Science (R0)