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Predicting missing links via local information

  • Zhou, Tao Research Center for Complex System Science, University of Shanghai for Science and Technology, China - Department of Physics, University of Fribourg, Switzerland - Department of Modern Physics, University of Science and Technology of China, Hefei, China
  • Lü, Linyuan Research Center for Complex System Science, University of Shanghai for Science and Technology, China - Department of Physics, University of Fribourg, Switzerland
  • Zhang, Yi-Cheng Research Center for Complex System Science, University of Shanghai for Science and Technology, China - Department of Physics, University of Fribourg, Switzerland - Department of Modern Physics, University of Science and Technology of China, Hefei, China
    10.10.2009
Published in:
  • The European Physical Journal B. - 2009, vol. 71, no. 4, p. 623-630
English Missing link prediction in networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well- known local similarity measures on six real networks. The results indicate that the simplest measure, namely Common Neighbours, has the best overall performance, and the Adamic-Adar index performs second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbours. It is found that many links are assigned the same scores if only the information of the nearest neighbours is used. We therefore design another new measure exploiting information on the next nearest neighbours, which can remarkably enhance the prediction accuracy.
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/301330
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