| Full text | |
| Author(s): |
Barboni Miranda, Gisele Helena
;
Machicao, Jeaneth
;
Bruno, Odemir Martinez
Total Authors: 3
|
| Document type: | Journal article |
| Source: | SCIENTIFIC REPORTS; v. 6, NOV 22 2016. |
| Web of Science Citations: | 3 |
| Abstract | |
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) | |
| FAPESP's process: | 15/05899-7 - Pattern recognition in complex networks through automata |
| Grantee: | Gisele Helena Barboni Miranda |
| Support type: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity |
| Grantee: | Odemir Martinez Bruno |
| Support type: | Regular Research Grants |