Community structures and role detection in music networks

TL;DR

This paper investigates community structures in music networks derived from similarity and collaboration data, revealing genre-based clusters, geographic and band-related communities, and identifying influential artists within these groups.

Contribution

It introduces a detailed analysis of community detection and role identification in music networks, highlighting organizational patterns and driving forces behind network growth.

Findings

Clusters correlate with musical genres in similarity networks

Small communities relate to bands and geographic zones

Large communities form around collaborative clusters

Abstract

We analyze the existence of community structures in two different social networks obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes.