Complex network classification using partially self-avoiding deterministic walks

Autor(es)

Goncalves, Wesley Nunes ^[1] ; Martinez, Alexandre Souto ^[2] ; Bruno, Odemir Martinez ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, IFSC, Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, FFCLRP, Natl Inst Sci & Technol Complex Syst LNCT SC, BR-14049 Ribeirao Preto, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Chaos; v. 22, n. 3 SEP 2012.
• Citações Web of Science: 7

Resumo

Complex networks have attracted increasing interest from various fields of science. It has been demonstrated that each complex network model presents specific topological structures which characterize its connectivity and dynamics. Complex network classification relies on the use of representative measurements that describe topological structures. Although there are a large number of measurements, most of them are correlated. To overcome this limitation, this paper presents a new measurement for complex network classification based on partially self-avoiding walks. We validate the measurement on a data set composed by 40000 complex networks of four well-known models. Our results indicate that the proposed measurement improves correct classification of networks compared to the traditional ones. (C) 2012 American Institute of Physics. {[}http://dx.doi.org/10.1063/1.4737515] (AU)

• Processo FAPESP: 11/01523-1 - Métodos de visão computacional aplicados à identificação e análise de plantas
• Beneficiário: Odemir Martinez Bruno
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 10/08614-0 - Análise de texturas estáticas e dinâmicas e suas aplicações em biologia e nanotecnologia
• Beneficiário: Wesley Nunes Gonçalves
• Linha de fomento: Bolsas no Brasil - Doutorado