title: Community structures and role detection in music networks.
abstract: We analyze the existence of community structures in two different social networks using data 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.
authors.affiliation: Departamento de Fisica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and CONICET, Buenos Aires, Argentina.
authors.forename: T
authors.initials: T
authors.lastname: Teitelbaum
authors.forename: P
authors.initials: P
authors.lastname: Balenzuela
authors.forename: P
authors.initials: P
authors.lastname: Cano
authors.forename: Javier M
authors.initials: JM
authors.lastname: Buldú
mesh_terms: D000465:Algorithms; D003142:Communication; D003198:Computer Simulation; D003299:Cooperative Behavior; D008954:Models, Biological; D009146:Music; D010363:Pattern Recognition, Automated / Q000379:methods*; D012944:Social Support