Uncovering collective listening habits and music genres in bipartite networks

Physics – Physics and Society

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

submitted to PRE

Scientific paper

10.1103/PhysRevE.72.066107

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.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Uncovering collective listening habits and music genres in bipartite networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Uncovering collective listening habits and music genres in bipartite networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Uncovering collective listening habits and music genres in bipartite networks will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-116703

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.