IDEAS home Printed from https://ideas.repec.org/a/hin/complx/2996750.html
   My bibliography  Save this article

Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering

Author

Listed:
  • Yi-Kun Zhao
  • Guo-Qing Wang
  • Xiao-Xiao Zhan
  • Peng-Hui Yang
  • Huihua Chen

Abstract

This paper makes a quantitative analysis of the comprehensive influence of music networks. Firstly, 11 music features are selected from energy, popularity, and other aspects to build a comprehensive evaluation index of music influence, and the PageRank algorithm is used to quantify the music influence. Secondly, the multiobjective logistic regression is used to construct the music similarity measurement model and, combined with music influence and music similarity, to judge whether the influence of different musicians is the actual influence. Thirdly, the influence and similarity of the same music genre and different music genres are analyzed by using the two-way cluster analysis method. Finally, the lasso region is used for feature selection to obtain the change factors in the process of music evolution and analyze the dynamic changes in the process of music development. Therefore, this paper uses network science to build a dynamic network to analyze the similarity of music, the evolution process, and the impact of music on culture, which has certain research significance and practical value in the fields of music, history, social science, and practice.

Suggested Citation

  • Yi-Kun Zhao & Guo-Qing Wang & Xiao-Xiao Zhan & Peng-Hui Yang & Huihua Chen, 2021. "Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering," Complexity, Hindawi, vol. 2021, pages 1-15, May.
    • Handle: RePEc:hin:complx:2996750
    DOI: 10.1155/2021/2996750
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/complexity/2021/2996750.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/complexity/2021/2996750.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/2996750?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
    ---><---

    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:hin:complx:2996750. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.