Faculté des sciences

Link prediction in complex networks: a local naïve Bayes model

Liu, Zhen ; Zhang, Qian-Ming ; Lü, Linyuan ; Zhou, Tao

In: Europhysics Letters, 2011, vol. 96, no. 4, p. 48007

The common-neighbor–based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In the traditional method, each common neighbor of two nodes contributes equally to the connection likelihood. In this letter, we argue that different common neighbors may play different roles and thus contributes... Plus

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    Summary
    The common-neighbor–based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In the traditional method, each common neighbor of two nodes contributes equally to the connection likelihood. In this letter, we argue that different common neighbors may play different roles and thus contributes differently, and propose a local naïve Bayes model. Extensive experiments were carried out on nine real networks. Compared with the traditional method, the present method can provide more accurate predictions.