Journal article

Bipartite network projection and personal recommendation

  • Zhou, Tao Department of Physics, University of Fribourg, Switzerland - Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, China
  • Ren, Jie Department of Physics, University of Fribourg, Switzerland
  • Medo, Matúš Department of Physics, University of Fribourg, Switzerland
  • Zhang, Yi-Cheng Department of Physics, University of Fribourg, Switzerland - Lab for Information Economy and Internet Research, Management School, University of Electronic Science and Technology of China, Chengdu Sichuan, China
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    25.10.2007
Published in:
  • Physical Review E. - 2007, vol. 76, no. 4, p. 046115
English One-mode projecting is extensively used to compress bipartite networks. Since one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method for compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do a personal recommendation.
Faculty
Faculté des sciences
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/300521
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