Paper Info
Title
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 musical works and artists. Data about user streaming behaviour and the musical collaboration networks are particularly important with newdata-driven recommendation systems. Here, we present a newcollaboration 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.
Year
DOI
Venue
2021
10.1093/comnet/cnaa050
JOURNAL OF COMPLEX NETWORKS
Keywords
DocType
Volume
collaboration networks, centrality, critical transitions, social network analysis
Journal
8
Issue
ISSN
Citations 
6
2051-1310
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Tobin South100.34
Matthew Roughan21638148.27
Lewis Mitchell315517.70