  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.
