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.
Highlights
The music market is an integral part of the cultural market, with a large audience and promising development prospects
A music company that invests in a potential musician and purchases a potential music record can earn considerable income
Based on the mutual enhancement relationship between the outstanding performance of musicians’ followers and the highly accomplished musicians’ artistic genres, this paper proposes an improved PageRank algorithm MRGT combined with the performance and popularity of emerging musicians in recent years
Summary
The music market is an integral part of the cultural market, with a large audience and promising development prospects. Series of indicators that measure the degree of change of music genres can be classified as social factors This can be evaluated through the existing massive data sets. Changes in the times in social factors that were difficult to quantify achieved quantification through distance We added this quantitative indicator to the influence model of musicians’. The DMRGT model proposed combines the advantages of macromodels, heuristic algorithms, and audio data analysis, adding the time decay function, the weight of genre influence, and audio data analysis influence factor to the traditional PageRank algorithm. Both social factors and non-social factors are included in the study. As a macromodel heuristic algorithm, the DMRGT model is explanatory, can handle batch data, and can avoid unfavorable factors, so as to provide fast speed and improved accuracy
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