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Toward link predictability of complex networks

Proceedings of the National Academy of Sciences - PNAS, 2015-02, Vol.112 (8), p.2325-2330 [Tạp chí có phản biện]

Volumes 1–89 and 106–112, copyright as a collective work only; author(s) retains copyright to individual articles ;Copyright National Academy of Sciences Feb 24, 2015 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1424644112 ;PMID: 25659742

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  • Nhan đề:
    Toward link predictability of complex networks
  • Tác giả: Lü, Linyuan ; Pan, Liming ; Zhou, Tao ; Zhang, Yi-Cheng ; Stanley, H. Eugene
  • Chủ đề: Algorithms ; Information technology ; Knowledge ; Physical Sciences
  • Là 1 phần của: Proceedings of the National Academy of Sciences - PNAS, 2015-02, Vol.112 (8), p.2325-2330
  • Mô tả: 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.
  • Nơi xuất bản: United States: National Academy of Sciences
  • Ngôn ngữ: English
  • Số nhận dạng: ISSN: 0027-8424
    EISSN: 1091-6490
    DOI: 10.1073/pnas.1424644112
    PMID: 25659742
  • Nguồn: GFMER Free Medical Journals
    PubMed Central
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