The optimization of musician influence model and the trait analysis of music in different genres

Abstract

For the purpose of better measuring the influence of musicians and exploring the similarities and characteristics of various genres, we need to optimize the Musician Influence Model properly and represent musical features in an appropriate way. The previous model was established based on the correction of followers’ and influencers’ number of each musician and the correction of total number of actual musicians according to genre proportion, which did not take the change of genre proportion over time and indirect influence of musician into consideration. To better optimize it, I innovatively introduced “direct number”, “indirect number” and “strong number” of followers and influencers, and all of them was corrected by proper functions. Through the standardization of data and the frequent use of entropy weight method, the relationship between the data became more accurate and reliable. The advantages a musician should have to be more influential can be assumed after obtaining the ranking table with Bob Dylan, The Beatles, Radiohead, Avril Lavigne and Alan Jackson being the top five. Besides, I selected six parameters - danceability, energy, valence, Tempo, acousticness and instrumentalness, to characterize the music. And I selected 6 representative genres based on the analysis of the proportion, musician number and average popularity of all genres. We draw some conclusions by analyzing the musical characteristics radar map of each genre.