Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering
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 influen...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/2996750 |
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| _version_ | 1832549914175864832 |
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| author | Yi-Kun Zhao Guo-Qing Wang Xiao-Xiao Zhan Peng-Hui Yang |
| author_facet | Yi-Kun Zhao Guo-Qing Wang Xiao-Xiao Zhan Peng-Hui Yang |
| author_sort | Yi-Kun Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-18a90258eb714a92b2296419e98576be |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-18a90258eb714a92b2296419e98576be2025-02-03T06:08:08ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/29967502996750Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional ClusteringYi-Kun Zhao0Guo-Qing Wang1Xiao-Xiao Zhan2Peng-Hui Yang3Art College, Anhui University of Finance and Economics, Bengbu 233030, ChinaSchool of Finance, Anhui University of Finance and Economics, Bengbu 233030, ChinaSchool of Law, Anhui University of Finance and Economics, Bengbu 233030, ChinaSchool of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, ChinaThis 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.http://dx.doi.org/10.1155/2021/2996750 |
| spellingShingle | Yi-Kun Zhao Guo-Qing Wang Xiao-Xiao Zhan Peng-Hui Yang Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering Complexity |
| title | Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering |
| title_full | Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering |
| title_fullStr | Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering |
| title_full_unstemmed | Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering |
| title_short | Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering |
| title_sort | quantitative analysis of comprehensive influence of music network based on logistic regression and bidirectional clustering |
| url | http://dx.doi.org/10.1155/2021/2996750 |
| work_keys_str_mv | AT yikunzhao quantitativeanalysisofcomprehensiveinfluenceofmusicnetworkbasedonlogisticregressionandbidirectionalclustering AT guoqingwang quantitativeanalysisofcomprehensiveinfluenceofmusicnetworkbasedonlogisticregressionandbidirectionalclustering AT xiaoxiaozhan quantitativeanalysisofcomprehensiveinfluenceofmusicnetworkbasedonlogisticregressionandbidirectionalclustering AT penghuiyang quantitativeanalysisofcomprehensiveinfluenceofmusicnetworkbasedonlogisticregressionandbidirectionalclustering |