Predicting missing links and identifying spurious links via likelihood analysis

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a net...

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Bibliographic Details
Published in:Scientific reports 2016-03, Vol.6 (1), p.22955-22955, Article 22955
Main Authors: Pan, Liming, Zhou, Tao, Lü, Linyuan, Hu, Chin-Kun
Format: Article
Language:English
Subjects:
639/705/1041
639/766/530/2801
639/766/530/2804
Algorithms
Computational Biology
Data processing
Humanities and Social Sciences
Humans
Models, Theoretical
multidisciplinary
Neural Networks (Computer)
Probability
Science
Social Networking
Social organization
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Summary:Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep22955