Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/20/2630 |
| _version_ | 1797514004517617664 |
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| author | Yiming Liu Longxin Wang Yunsong Jia Ziwen Li Hongju Gao |
| author_facet | Yiming Liu Longxin Wang Yunsong Jia Ziwen Li Hongju Gao |
| author_sort | Yiming Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:25:28Z |
| format | Article |
| id | doaj.art-f22ce7706f4a4ffdbb70afa971412284 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T06:25:28Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-f22ce7706f4a4ffdbb70afa9714122842023-11-22T19:02:55ZengMDPI AGMathematics2227-73902021-10-01920263010.3390/math9202630Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information NetworkYiming Liu0Longxin Wang1Yunsong Jia2Ziwen Li3Hongju Gao4College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaSocial 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.https://www.mdpi.com/2227-7390/9/20/2630influence rankingPageRank algorithmGaussian distancethe similarity of music genreHP filter |
| spellingShingle | Yiming Liu Longxin Wang Yunsong Jia Ziwen Li Hongju Gao Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network Mathematics influence ranking PageRank algorithm Gaussian distance the similarity of music genre HP filter |
| title | Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network |
| title_full | Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network |
| title_fullStr | Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network |
| title_full_unstemmed | Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network |
| title_short | Dynamic Influence Ranking Algorithm Based on Musicians’ Social and Personal Information Network |
| title_sort | dynamic influence ranking algorithm based on musicians social and personal information network |
| topic | influence ranking PageRank algorithm Gaussian distance the similarity of music genre HP filter |
| url | https://www.mdpi.com/2227-7390/9/20/2630 |
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