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Mapping of science journals based on h-similarity

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Abstract

Journals covered by the 2006 Science Citation Index Journal Citation Reports database have been subjected to a clustering procedure utilizing h-similarity as the underlying similarity measure. Clustering complemented with a prototyping routine provided well-conceivable results that are both compatible with and further refine existing taxonomies of science.

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References

  • Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems 1695. http://igraph.sf.net.

  • Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99, 7821–7826.

    Article  MATH  MathSciNet  Google Scholar 

  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572 (also available at: arXiv: physics/0508025, accessible via http://arxiv.org/abs/physics/0508025).

  • Janssens, F., Zhang, L., De Moor, B., & Glänzel, W. (2009). Hybrid clustering for validation and improvement of subject-classification schemes. Information Processing and Management, 45, 683–702.

    Article  Google Scholar 

  • Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103(23), 8577–8582.

    Article  Google Scholar 

  • Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physics Reviews E, 69, 026113.

    Article  Google Scholar 

  • Pons, P., & Csardi, G. (no date). http://igraph.sourceforge.net/doc/R/walktrap.community.html.

  • Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks, http://arxiv.org/abs/physics/0512106.

  • R Development Core Team. (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

  • Schubert, A. (2010). A reference-based Hirschian similarity measure for journals. Scientometrics, doi:10.1007/s11192-009-0072-4.

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Authors and Affiliations

  1. Institute for Research Policy Studies, Hungarian Academy of Sciences, Budapest, Hungary

    András Schubert & Sándor Soós

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  1. András Schubert
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  2. Sándor Soós
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Correspondence to András Schubert.

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Schubert, A., Soós, S. Mapping of science journals based on h-similarity. Scientometrics 83, 589–600 (2010). https://doi.org/10.1007/s11192-010-0167-y

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  • DOI: https://doi.org/10.1007/s11192-010-0167-y

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