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

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.

Fintech Challenges and Opportunities in Banking

“…Musicians now are able to be visible and form relationships with audiences (Psomadaki et al, 2022) as well as to measure their influences (Y. Liu et al, 2021), an emerging virtual reality world opens up new business ventures (Bossey, 2022). What was once controlled by a set group of businessmen who owed music labels, now is in hands of every musician who is able to employ the use of Spotify (Eriksson, 2020).…”