2005/08/31 by Renaud Lambiotte, R. Lambiotte, Marcel Ausloos +1 · 3 citations
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Music and Audio Processing #Opinion Dynamics and Social Influence #physics.soc-ph
paper · pdf · doi:10.1103/physreve.72.066107
published as Phys. Rev. E 72, 066107 (2005) · submitted to PRE
arxiv created 2005/10/24 · openalex publication_date 2005/12/07 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we analyze web-downloaded data on people sharing their music library, that we use as their individual musical signatures. 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 visualized 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 and 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 for growing bipartite networks is outlined, following Potts ingredients. Simulation results do reproduce quite well the empirical data.