Popularity and Centrality in Spotify Networks: Critical transitions in eigenvector centrality

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 music together. Data about user streaming behaviour, and the musical collaboration networks are particularly important with new data-driven recommendation systems. Without thorough analysis, such collaboration graphs can lead to false or misleading conclusions. 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. The critical change in centrality, from classical artists to rap artists, demonstrates deeper structural properties of the network. A Social Group Centrality model is presented to simulate this critical transition behaviour, and switching between dominant eigenvectors is observed. This model presents a novel investigation of the effect of popularity bias on how centrality and importance are measured, and provides a new tool for examining such flaws in networks.