Dataset Information

Predicting missing links and identifying spurious links via likelihood analysis.

ABSTRACT: 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.

SUBMITTER: Pan L

PROVIDER: S-EPMC4785364 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Predicting missing links and identifying spurious links via likelihood analysis.

Pan Liming L Zhou Tao T Lü Linyuan L Hu Chin-Kun CK

Scientific reports 20160310

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 probab ...[more]

PMID: 26961965

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Dataset Information

Predicting missing links and identifying spurious links via likelihood analysis.

Publications

Predicting missing links and identifying spurious links via likelihood analysis.

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