Popularity and centrality in Spotify networks: critical transitions in eigenvector centrality

Abstract

Abstract The modern age of digital music access has increased the availability of data about music consumption and creation, facilitating the large-scale analysis of the complex networks that connect musical works and artists. Data about user streaming behaviour and the musical collaboration networks are particularly important with new data-driven recommendation systems. Here, we present a new collaboration network of artists from the online music streaming service Spotify and demonstrate a critical change in the eigenvector centrality of artists, as low popularity artists are removed. This critical change in centrality, from a central core of classical artists to a core of rap artists, demonstrates deeper structural properties of the network. Both the popularity and degree of collaborators play an important role in the centrality of these groups. Rap artists have dense collaborations with other popular artists whereas classical artists are diversely connected to a large number of low and medium popularity artists throughout the graph through renditions and compilations. A Social Group Centrality model is presented to simulate this critical transition behaviour, and switching between dominant eigenvectors is observed. By contrasting a group of high-degree diversely connected community leaders to a group of celebrities which only connect to high popularity nodes, this model presents a novel investigation into the effect of popularity bias on how centrality and importance are measured.