MFCSNet: A Musician-Follower Complex Social Network for Measuring Musical Influence

Music, a significant and exquisite part of human culture, owns abundant features and enjoys a long-standing history. Music evolves in society over time, while artists' music gets influenced by personal experiences, external events, and inspirations from predecessors. In this paper, we propose a model named MFCSNet that measures musical influence by utilizing the data sets of musical characteristics and links between music influencers and followers. MFCSNet applies multiple indicators and has more analysis perspectives, and well reflects the influence of different kinds of music in various fields. Firstly, we analyze the influencer- follower relations by looking at the network of musical influence, observing the correlation between followers and influencers, and closely examining several sub-networks extracted from the entire network. Secondly, we propose measures that quantify the similarities within and between musical genres, using musical characteristics, such as danceability, energy, and valence, in order to measure the influence between artists and find the more influential characteristics. Furthermore, we apply MFCSNet on the whole timeline to analyze the evolutions and revolutions of music through time, with the goal of revealing the relation between music and culture, society, politics, and technologies.