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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

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Autor(es):
Barboni Miranda, Gisele Helena ; Machicao, Jeaneth ; Bruno, Odemir Martinez
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: SCIENTIFIC REPORTS; v. 6, NOV 22 2016.
Citações Web of Science: 0
Resumo

Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability. (AU)

Processo FAPESP: 15/05899-7 - Reconhecimento de padrões em redes complexas por meio de autômatos
Beneficiário:Gisele Helena Barboni Miranda
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 14/08026-1 - Visão artificial e reconhecimento de padrões aplicados em plasticidade vegetal
Beneficiário:Odemir Martinez Bruno
Linha de fomento: Auxílio à Pesquisa - Regular