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Link prediction in complex networks: A survey
Authors:Linyuan Lü  Tao Zhou
Institution:
  • a Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, People’s Republic of China
  • b Research Center for Complex System Science, University of Shanghai for Science and Technology, Shanghai 200093, People’s Republic of China
  • c Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland
  • d Department of Modern Physics, University of Science and Technology of China, Hefei 230026, People’s Republic of China
  • Abstract:Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.
    Keywords:Link prediction  Complex networks  Node similarity  Maximum likelihood methods  Probabilistic models
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