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 (...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | Brasil |
| Institución: | Universidade Federal do Rio Grande do Norte (UFRN) |
| Repositorio: | Repositório Institucional da UFRN |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ufrn.br:123456789/30641 |
| Acceso en línea: | https://repositorio.ufrn.br/handle/123456789/30641 |
| Access Level: | acceso abierto |
| Palabra clave: | Generalized statistics Degree distribution Networks |
| Sumario: | 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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