dorsal/arxiv
View SchemaUncovering collective listening habits and music genres in bipartite networks
| Authors | R. Lambiotte, M. Ausloos |
|---|---|
| Categories | |
| ArXiv ID | physics/0508233 |
| URL | https://arxiv.org/abs/physics/0508233 |
| DOI | 10.1103/PhysRevE.72.066107 |
| Journal | Phys. Rev. E 72, 066107 (2005) |
Abstract
In 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.
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"abstract": "In this paper, we analyze web-downloaded data on people sharing their music\nlibrary, that we use as their individual musical signatures (IMS). The system\nis represented by a bipartite network, nodes being the music groups and the\nlisteners. Music groups audience size behaves like a power law, but the\nindividual music library size is an exponential with deviations at small\nvalues. In order to extract structures from the network, we focus on\ncorrelation matrices, that we filter by removing the least correlated links.\nThis percolation idea-based method reveals the emergence of social communities\nand music genres, that are visualised by a branching representation. Evidence\nof collective listening habits that do not fit the neat usual genres defined by\nthe music industry indicates an alternative way of classifying listeners/music\ngroups. The structure of the network is also studied by a more refined method,\nbased upon a random walk exploration of its properties. Finally, a personal\nidentification - community imitation model (PICI) for growing bipartite\nnetworks is outlined, following Potts ingredients. Simulation results do\nreproduce quite well the empirical data.",
"arxiv_id": "physics/0508233",
"authors": [
"R. Lambiotte",
"M. Ausloos"
],
"categories": [
"physics.soc-ph"
],
"doi": "10.1103/PhysRevE.72.066107",
"journal_ref": "Phys. Rev. E 72, 066107 (2005)",
"title": "Uncovering collective listening habits and music genres in bipartite networks",
"url": "https://arxiv.org/abs/physics/0508233"
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