Collaborative Tagging as a Tripartite Network

@article{Lambiotte2006CollaborativeTA,
  title={Collaborative Tagging as a Tripartite Network},
  author={Renaud Lambiotte and Marcel Ausloos},
  journal={ArXiv},
  year={2006},
  volume={abs/cs/0512090}
}
We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed. 

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References

SHOWING 1-3 OF 3 REFERENCES
Phys
  • Rev. E, 72, 066107
  • 2005
Uncovering collective listening habits and music genres in bipartite networks.
  • R. Lambiotte, M. Ausloos
  • Computer Science, Physics
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2005
TLDR
Analysis of web-downloaded data on people sharing their music library shows 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.
Rev
  • of Mod. Phys., 74
  • 2002