Journal article

Link prediction in bipartite nested networks

  • Medo, Matúš Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology, Chengdu, China - Department of Radiation Oncology, University of Bern, Switzerland - Department of Physics, University of Fribourg, Switzerland
  • Mariani, Manuel Sebastian Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology, Chengdu, China - URPP Social Networks, Universität Zürich, Switzerland
  • Lü, Linyuan Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology, Chengdu, China - Alibaba Research Center, Hangzhou Normal University, China
    10.10.2018
Published in:
  • Entropy. - 2018, vol. 20, no. 10, p. 777
English Real networks typically studied in various research fields—ecology and economic complexity, for example—often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well- established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/307480
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