[Paper]: Quantifying Long-Term Scientific Impact

Science 4 October 2013:
Vol. 342 no. 6154 pp. 127-132
DOI: 10.1126/science.1237825

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Quantifying Long-Term Scientific Impact

  1. Dashun Wang1,2,*,
  2. Chaoming Song1,3,*,
  3. Albert-László Barabási1,4,5,6,

+ Author Affiliations


  1. 1Center for Complex Network Research, Department of Physics, Department of Biology, and Department of Computer Science, Northeastern University, Boston, MA 02115, USA.

  2. 2IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA.

  3. 3Department of Physics, University of Miami, Coral Gables, FL 33124, USA.

  4. 4Center for Cancer Systems Biology, Dana Farber Cancer Institute, Boston, MA 02115, USA.

  5. 5Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.

  6. 6Center for Network Science, Central European University, Budapest, Hungary.
  1. †Corresponding author. E-mail: alb@neu.edu
  1. * These authors contributed equally to the work.

The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications…

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