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Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network

Author

Listed:
  • Yiming Liu

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Longxin Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Yunsong Jia

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Ziwen Li

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Hongju Gao

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

Abstract

Social influence analysis is a very popular research direction. This article analyzes the social network of musicians and the many influencing factors when musicians create music to rank the influence of musicians. In order to achieve the practical purpose of the model making accurate predictions in the broad music market, the algorithm adopts a macromodel and considers the social network topology network. The article adds the time decay function and the weight of genre influence to the traditional PageRank algorithm, and thus, the MRGT (Musician Ranking based on Genre and Time) algorithm appears. Considering the timeliness of social networks and the continuous development of music, we realized the importance of evolving MRGT into a dynamic social network. Therefore, we adopted audio data analysis technology and used Gaussian distance to classify and study the evolution of music properties at different times and different genres and finally formed the dynamic influence ranking algorithm based on musicians’ social and personal information networks. As a macromodel heuristic algorithm, our model is explanatory, can handle batch data and can avoid unfavorable factors, so as to provide fast speed and improved accuracy. The network can obtain an era indicator DMI (Dynamic Music Influence) that measures the degree of music revolution. DMI is the indicator we provide for music companies to invest in musicians.

Suggested Citation

  • Yiming Liu & Longxin Wang & Yunsong Jia & Ziwen Li & Hongju Gao, 2021. "Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2630-:d:659034
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    References listed on IDEAS

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    1. Leon du Toit, 2008. "Optimal HP filtering for South Africa," Working Papers 07/2008, Stellenbosch University, Department of Economics.
    2. Xia, Ling-Ling & Jiang, Guo-Ping & Song, Bo & Song, Yu-Rong, 2015. "Rumor spreading model considering hesitating mechanism in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 295-303.
    3. Suman Kumar Saha & R. Kar & D. Mandal & S. P. Ghoshal, 2013. "A Novel Firefly Algorithm for Optimal Linear Phase FIR Filter Design," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 4(2), pages 29-48, April.
    4. Li, Pei & Liu, Ke & Li, Keqin & Liu, Jianxun & Zhou, Dong, 2021. "Estimating user influence ranking in independent cascade model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    5. Maymin, Philip, 2012. "Music and the market: Song and stock volatility," The North American Journal of Economics and Finance, Elsevier, vol. 23(1), pages 70-85.
    6. Bo Zhang & Yufeng Wang & Qun Jin & Jianhua Ma, 2015. "A Pagerank-Inspired Heuristic Scheme for Influence Maximization in Social Networks," International Journal of Web Services Research (IJWSR), IGI Global, vol. 12(4), pages 48-62, October.
    7. Zhao, Laijun & Xie, Wanlin & Gao, H. Oliver & Qiu, Xiaoyan & Wang, Xiaoli & Zhang, Shuhai, 2013. "A rumor spreading model with variable forgetting rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6146-6154.
    8. Xiaxia Zhao & Jianzhong Wang, 2013. "Dynamical Model about Rumor Spreading with Medium," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-9, March.
    Full references (including those not matched with items on IDEAS)

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