Topological analysis of interdisciplinary scientific journals: which journals will be the next nature or science?

ABSTRACT

Identifying prestigious interdisciplinary journals is very significant for researchers. By publishing research works in prestigious journals, researchers can better propagate their works and get spotlights. Even though the quality of a paper is not represented by the journal that publish the paper, it is a general concern of researchers that how to identify a set of good journals to submit their papers. Nature and Science are the two journals that have been considering as the two top interdisciplinary journals worldwide. In this paper, we propose a method for identifying journals that have the potential to become the next Nature and Science through topological analysis of interdisciplinary scientific journals using citation data. By applying three different statistical methods (i.e., Multidimensional scaling, Principal component analysis, Cluster analysis), we identified a set of journals in which PNAS has the highest possibility to become the next Nature and Science. The study showed that citation data is a powerful data to measure similarity among journals.

References

1. Bergstrom, C. T., & West, J. D. (2008). Assessing citations with the Eigenfactor™ Metrics. Neurology, 71(23), 1850--1851.
2. Bollen, J., Rodriguez, M. A., & Van de Sompel, H. (2006). Journal status. Scientometrics, 69(3), 669--687.
3. Borg, I., & Groenen, P. J. F. (1997). Modern multidimensional scaling: Theory and applications. New York: Springer.
4. Carpenter, M. P., & Narin, F. (1973). Clustering of scientific journals. Journal of the American Society for Information Science, 24(6), 425--436.
5. Dellavalle, R. P., Schilling, L. M., Rodriguez, M. A., Van de Sompel, H., & Bollen, J. (2007). Refining dermatology journal impact factors using PageRank. Journal of the American Academy of Dermatology, 57(1), 116--119.
6. Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241--254.
7. Jolliffe, I. T., & NetLibrary, I. (2002). Principal component analysis (Secondition.; 2nd ed.). New York: Springer.
8. Leydesdorff, L. (2009). How are new citation-based journal indicators adding to the bibliometric toolbox? Journal of the American Society for Information Science and Technology, 60(7), 1327--1336.
9. SCImago (2007). SJR: SCImago Journal & Country Rank. Retrieved August 31, 2009 from http://www.scimagojr.com
10. White, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972-1995. Journal of the American society for information science, 49(4), 327--355.
11. Zhu, Y., & Yan, E. (2015). Dynamic subfield analysis of disciplines: An examination of the trading impact and knowledge diffusion patterns of computer science. Scientometrics, 104(1), 335--359.

Index Terms

• Information systems

  • Information systems applications

    • Data mining

• Mathematics of computing

  • Probability and statistics

    • Statistical paradigms