Bipartite network analysis of sample-based music

Abstract

Musical sampling is a composition technique of popular music where one borrows elements from existing recordings to produce new songs. The sampled music is thus deeply related to the newly created music based on it. We can, therefore, surmise that the sampling practice of an artist reflects the characteristics of the subgenre or the music community which the artist belongs operates in and belongs to, implying that the sampling relationships can help us understand the origin and evolution of many different styles of sample-based music. In this study, we present a complex network analysis of the communities of artists connected via sampling relationships. We establish an artist-sample bipartite network of artists who perform the sampling, and the songs that are the subjects of sampling. The detection of communities composed of artists and songs demonstrates that the sample-based musical scene has a clear community structure where each community features artists with high centrality that allows us to identify the musical styles of the community. While this study focuses on the understanding of sample-based music that forms the basis of an overwhelming majority of contemporary popular musical paradigms, we believe this framework is general enough to be applied to many other creative fields that involve referencing of existing works.