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

TL;DR

This paper analyzes Spotify artist networks, revealing a critical transition in eigenvector centrality from classical to rap artists as less popular artists are removed, highlighting structural properties and biases in music collaboration networks.

Contribution

It introduces a new Spotify artist collaboration network and a Social Group Centrality model to study critical transitions and biases in eigenvector centrality.

Findings

Critical transition in eigenvector centrality from classical to rap artists

Switching between dominant eigenvectors observed in the model

Highlights structural properties and popularity bias in music networks

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.