Yinglin Ge, Yuxuan He*
School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China.
*Corresponding author: Yuxuan He
Abstract
Music is an indispensable part of our life, affects every aspect of social life. Quantifying music changes and influences plays a big role in the understanding of existing music and optimization of future music development. This paper, using the data given by 2021ICM, builds a model to value the influence of music and uses it to analysis music evolution as well as the relationship between the musicians and music genres. Creating model based on the PageRank algorithm along with the calculation method of the paper’s impact factor (IF) can measure the importance (influence level) of nodes (individual musicians) in complex network and achieve the dual construction of qualitative and quantitative network. Moreover, the paper establishes the model of similarity metrics in order to measure music similarity. Pearson correlation coefficient is used to assess the similarity of songs in the same genre and the similarity of different artists, accompanied with the aid of Python to process large amounts of data.
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How to cite this paper
Research on Importance and Correlation Evaluation of Music Network Nodes Based on Page Rank Algorithm
How to cite this paper: Yinglin Ge, Yuxuan He. (2022) Research on Importance and Correlation Evaluation of Music Network Nodes Based on Page Rank Algorithm. Journal of Applied Mathematics and Computation, 6(2), 267-272.
DOI: http://dx.doi.org/10.26855/jamc.2022.06.012