Link prediction in complex networks: a local naïve Bayes model
- Liu, Zhen Web Sciences Center, University of Electronic Science and Technology of China - Chengdu, PRC
- Zhang, Qian-Ming Web Sciences Center, University of Electronic Science and Technology of China - Chengdu, PRC
- Lü, Linyuan Department of Physics, University of Fribourg, Switzerland
- Zhou, Tao Web Sciences Center, University of Electronic Science and Technology of China - Chengdu, PRC
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11.11.2011
Published in:
- Europhysics Letters. - 2011, vol. 96, no. 4, p. 48007
English
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.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Physique
- Language
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- English
- Classification
- Physics
- License
- License undefined
- Identifiers
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- RERO DOC 28321
- DOI 10.1209/0295-5075/96/48007
- Persistent URL
- https://folia.unifr.ch/unifr/documents/302207
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