Quantifying Long-Term Scientific Impact

Author:

Wang Dashun12,Song Chaoming13,Barabási Albert-László1456

Affiliation:

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

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

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

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

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

6. Center for Network Science, Central European University, Budapest, Hungary.

Abstract

Citation Grabbers Is there quantifiable regularity and predictability in citation patterns? It is clear that papers that have been cited frequently tend to accumulate more citations. It is also clear that, with time, even the most novel paper loses its currency. Some papers, however, seem to have an inherent “fitness” that can be interpreted as a community's response to the research. Wang et al. (p. 127 ; see the Perspective by Evans ) developed a mechanistic model to predict citation history. The model links a paper's ultimate impact, represented by the total number of citations the paper will ever receive, to a single measurable parameter inferred from its early citation history. The model was used to identify factors that influence a journal's impact factor.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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