IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0120735.html
   My bibliography  Save this article

Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future

Author

Listed:
  • Yanbo Zhou
  • An Zeng
  • Wei-Hong Wang

Abstract

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.

Suggested Citation

  • Yanbo Zhou & An Zeng & Wei-Hong Wang, 2015. "Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-10, March.
    • Handle: RePEc:plo:pone00:0120735
    DOI: 10.1371/journal.pone.0120735
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0120735
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0120735&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0120735?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yanbo Zhou & Hongbing Cheng & Qu Li & Weihong Wang, 2020. "Diversity of temporal influence in popularity prediction of scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 383-392, April.
    2. Xianlong Zhao & Xianze Xu & He Nai & Chen Zhou & Zhiyi Hu & Yi Zhang & Hao Jiang, 2018. "Analysis of behavioral differentiation in smart cities based on mobile users’ usage detail record data," International Journal of Distributed Sensor Networks, , vol. 14(4), pages 15501477187, April.
    3. Sidorov, Sergei & Mironov, Sergei, 2021. "Growth network models with random number of attached links," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    4. Pongnumkul, Suchit & Motohashi, Kazuyuki, 2018. "A bipartite fitness model for online music streaming services," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1125-1137.
    5. Abbas, Khushnood & Shang, Mingsheng & Luo, Xin & Abbasi, Alireza, 2017. "Emerging trends in evolving networks: Recent behaviour dominant and non-dominant model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 506-515.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0120735. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.