Uncovering collective listening habits and music genres in bipartite networks

Simple item page
dc.creatorLambiotte, R.
dc.creatorAusloos, M.
dc.date2005-08-31
dc.date2005-10-24
dc.date.accessioned2026-07-07T08:08:58Z
dc.date.available2026-07-07T08:08:58Z
dc.descriptionIn this paper, we analyze web-downloaded data on people sharing their music library, that we use as their individual musical signatures (IMS). The system is represented by a bipartite network, nodes being the music groups and the listeners. Music groups audience size behaves like a power law, but the individual music library size is an exponential with deviations at small values. In order to extract structures from the network, we focus on correlation matrices, that we filter by removing the least correlated links. This percolation idea-based method reveals the emergence of social communities and music genres, that are visualised by a branching representation. Evidence of collective listening habits that do not fit the neat usual genres defined by the music industry indicates an alternative way of classifying listeners/music groups. The structure of the network is also studied by a more refined method, based upon a random walk exploration of its properties. Finally, a personal identification - community imitation model (PICI) for growing bipartite networks is outlined, following Potts ingredients. Simulation results do reproduce quite well the empirical data.
dc.descriptionsubmitted to PRE
dc.identifierhttps://arxiv.org/abs/physics/0508233
dc.identifierhttp://arxiv.org/abs/physics/0508233
dc.identifierPhys. Rev. E 72, 066107 (2005)
dc.identifierdoi:10.1103/PhysRevE.72.066107
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131445
dc.subjectPhysics and Society
dc.titleUncovering collective listening habits and music genres in bipartite networks
dc.typetext

Files

Collections

arXiv