Maximum entropy principle for Kaniadakis statistics and networks
In this Letter we investigate a connection between Kaniadakis power-law statistics and networks. By following the maximum entropy principle, we maximize the Kaniadakis entropy and derive the optimal degree distribution of complex networks. We show that the degree distribution follows P(k) =P0 expκ (...
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| Principais autores: | , , , |
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| Formato: | article |
| Idioma: | English |
| Publicado em: |
Elsevier
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/30641 |
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| Resumo: | In this Letter we investigate a connection between Kaniadakis power-law statistics and networks. By following the maximum entropy principle, we maximize the Kaniadakis entropy and derive the optimal degree distribution of complex networks. We show that the degree distribution follows P(k) =P0 expκ (−k/ηκ ) with expκ (x) = (√1 + κ2x2 + κx)1/κ , and |κ| < 1. In order to check our approach we study a preferential attachment growth model introduced by Soares et al. [Europhys. Lett. 70 (2005) 70] and a growing random network (GRN) model investigated by Krapivsky et al. [Phys. Rev. Lett. 85 (2000) 4629]. Our results are compared with the ones calculated through the Tsallis statistics |
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