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Toward link predictability of complex networks

Lü, Linyuan ; Pan, Liming ; Zhou, Tao ; Zhang, Yi-Cheng ; Stanley, H. Eugene

Proceedings of the National Academy of Sciences - PNAS, 2015-02, Vol.112 (8), p.2325-2330 [Periódico revisado por pares]

United States: National Academy of Sciences

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  • Título:
    Toward link predictability of complex networks
  • Autor: Lü, Linyuan ; Pan, Liming ; Zhou, Tao ; Zhang, Yi-Cheng ; Stanley, H. Eugene
  • Assuntos: Algorithms ; Information technology ; Knowledge ; Physical Sciences
  • É parte de: Proceedings of the National Academy of Sciences - PNAS, 2015-02, Vol.112 (8), p.2325-2330
  • Notas: http://dx.doi.org/10.1073/pnas.1424644112
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    SourceType-Scholarly Journals-1
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    Contributed by H. Eugene Stanley, December 31, 2014 (sent for review March 10, 2014; reviewed by Giorgio Parisi and Dashun Wang)
    Reviewers: G.P., University of Rome; and D.W., IBM TJ Watson Research Center.
    1L.L., L.P., and T.Z. contributed equally to this work.
    Author contributions: L.L., L.P., T.Z., Y.-C.Z., and H.E.S. designed research; L.L., L.P., Y.-C.Z., and H.E.S. performed research; L.L., L.P., T.Z., and Y.-C.Z. analyzed data; and L.L., T.Z., Y.-C.Z., and H.E.S. wrote the paper.
  • Descrição: The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that ( i ) structural consistency is a good estimation of link predictability and ( ii ) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners. Significance Quantifying a network's link predictability allows us to ( i ) evaluate predictive algorithms associated with the network, ( ii ) estimate the extent to which the organization of the network is explicable, and ( iii ) monitor sudden mechanistic changes during the network's evolution. The hypothesis of this paper is that a group of links is predictable if removing them has only a small effect on the network's structural features. We introduce a quantitative index for measuring link predictability and an algorithm that outperforms state-of-the-art link prediction methods in both accuracy and universality. This study provides fundamental insights into important scientific problems and will aid in the development of information filtering technologies.
  • Editor: United States: National Academy of Sciences
  • Idioma: Inglês
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