IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v507y2018icp110-122.html
   My bibliography  Save this article

Forecasting turning trends in knowledge networks

Author

Listed:
  • Szántó-Várnagy, Ádám
  • Farkas, Illés J.

Abstract

A large portion of our collective human knowledge is in electronic repositories. These repositories range from “hard fact” databases (e.g., scientific publications and patents) to “soft” knowledge such as news portals. The common denominator between them all is that they can be thought of in terms of topics/keywords. The interest in these topics is constantly changing over time. Their frequency occurrence diagrams can be used for effective prediction by the most straightforward simplification. In this paper, we use these diagrams to produce simple and human-readable rules that are able to predict the future trends of the most important keywords in 5 data sets of different types. A thorough analysis of the necessary input variables and parameters and their relation to the success rate is presented, as well.

Suggested Citation

  • Szántó-Várnagy, Ádám & Farkas, Illés J., 2018. "Forecasting turning trends in knowledge networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 110-122.
    • Handle: RePEc:eee:phsmap:v:507:y:2018:i:c:p:110-122
    DOI: 10.1016/j.physa.2018.05.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118305995
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.05.055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:507:y:2018:i:c:p:110-122. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.