Volume 96, Number 4, November 2011
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||11 November 2011|
Link prediction in complex networks: A local naïve Bayes model
Web Sciences Center, University of Electronic Science and Technology of China - Chengdu 610054, PRC
2 Department of Physics, University of Fribourg - Chemin du Musée 3, Fribourg CH-1700, Switzerland
Accepted: 6 October 2011
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.
PACS: 89.75.Hc – Networks and genealogical trees / 89.20.Ff – Computer science and technology / 89.65.-s – Social and economic systems
© EPLA, 2011
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