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Citation Networks

Part of the Understanding Complex Systems book series (UCS)

Abstract

Bibliographic databases contain a huge amount of information on the dissemination of scientific knowledge and the relationships between papers, authors, and scientific work. Large-scale citation networks can be generated from these databases in order to provide a systems-level perspective on the processes at the root of the spreading of ideas, theories, and results in science. Citation networks are therefore one of the main proxies for our understanding of knowledge dynamics as well as invaluable systems for the quantitative analysis of the impact of specific scientific contributions, the emergence of technical and scientific areas, and the ranking of journals, institutions, and scientists. This chapter reviews recent developments made in the study of citation networks, ranging from empirical analyses of real systems and mathematical models of them, to the study of dynamic processes taking place in them and their potential applications. Furthermore, studying citation datasets with the tools of network theory opens new avenues towards a quantitative understanding of the dynamics of popularity with respect to papers, journals, and scientists, possibly leading to novel measures of impact and ranking.

Keywords

  • Citation Count
  • Community Detection
  • Preferential Attachment
  • Citation Network
  • Bibliographic Database

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    WoS: Web of Science, URL: http://isiknowledge.com/WOS.

  2. 2.

    Crossref, URL: http://www.crossref.org.

  3. 3.

    Scopus, URL: http://www.scopus.com.

  4. 4.

    Google Scholar, URL: http://scholar.google.com.

  5. 5.

    Citebase, URL: http://www.citebase.org.

  6. 6.

    Citeseer, URL: http://citeseer.ist.psu.edu.

  7. 7.

    SPIRES, URL: http://www.slac.stanford.edu/spires.

  8. 8.

    URL: http://www.researcherid.com.

  9. 9.

    NBER: The National Bureau of Economic Research, U.S. Patent Citations Data at the URL: http://www.nber.org/patents.

  10. 10.

    Google Patents, URL: http://www.google.com/patents.

  11. 11.

    EPO: European Patent Office, URL: http://www.epo.org/patents/patent-information.html.

  12. 12.

    Supreme Court of the United States, URL: http://www.supremecourt.gov.

  13. 13.

    American Physical Society, URL: http://publish.aps.org.

  14. 14.

    URL: http://arxiv.org.

  15. 15.

    Citerank, URL: http://www.cmth.bnl.gov/~maslov/citerank/index.php.

  16. 16.

    URL: http://www.eigenfactor.org.

  17. 17.

    SCImago, URL: http://www.scimagojr.com.

  18. 18.

    Phys Author Rank Algorithm, URL: http://physauthorsrank.org.

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Correspondence to Filippo Radicchi .

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© 2012 Springer-Verlag Berlin Heidelberg

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Radicchi, F., Fortunato, S., Vespignani, A. (2012). Citation Networks. In: Scharnhorst, A., Börner, K., van den Besselaar, P. (eds) Models of Science Dynamics. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23068-4_7

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  • DOI: https://doi.org/10.1007/978-3-642-23068-4_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23067-7

  • Online ISBN: 978-3-642-23068-4

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