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Pattern recognition in complex networks through automata

Grant number: 15/05899-7
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): July 01, 2015
Effective date (End): May 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
Principal researcher:Odemir Martinez Bruno
Grantee:Gisele Helena Barboni Miranda
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated scholarship(s):18/00147-5 - Evolutionary Pattern Recognition in Complex Biological Networks, BE.EP.DR


Research on complex networks has become multidisciplinary and has contributed to different areas of knowledge. Any discrete system can be represented by the interactions between their individuals and through the study of the mechanisms involving these interactions we can characterize and compare these interactions. This representation flexibility combined to the growing interest in the study of dynamical systems have motivated several studies in the area of complex networks. The progress in this area over the last decade helped the understanding of structural and dynamic characteristics of networks. These results open the way for research in pattern recognition area in complex networks, which still has great potential to be explored. Among the computational and mathematical tools for the study of complex systems are cellular automata and deterministic walks. Combined to complex networks these tools can be used to map the relationship between the network architecture and its dynamics regarding the perspective of pattern formation. The objective of this work is the proposal, implementation and analysis of methods for pattern recognition in complex networks using models based on automata. We intend to investigate the use of cellular automata and tourist walk, which have roots in automata theory, as means for feature extraction from networks. Thus, we identify in this project a great possibility of contribution with new methods in the area of complex networks analysis. (AU)

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Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MIRANDA, GISELE H. B.; BAETENS, JAN M.; BOSSUYT, NATHALIE; BRUNO, ODEMIR M.; DE BAETS, BERNARD. Real-time prediction of influenza outbreaks in Belgium. EPIDEMICS, v. 28, SEP 2019. Web of Science Citations: 0.
MIRANDA, GISELE H. B.; MACHICAO, JEANETH; BRUNO, ODEMIR M. An optimized shape descriptor based on structural properties of networks. DIGITAL SIGNAL PROCESSING, v. 82, p. 216-229, NOV 2018. Web of Science Citations: 0.
MACHICAO, JEANETH; CORREA, JR., EDILSON A.; MIRANDA, GISELE H. B.; AMANCIO, DIEGO R.; BRUNO, ODEMIR M. Authorship attribution based on Life-Like Network Automata. PLoS One, v. 13, n. 3 MAR 22 2018. Web of Science Citations: 0.
BARBONI MIRANDA, GISELE HELENA; MACHICAO, JEANETH; BRUNO, ODEMIR MARTINEZ. Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks. SCIENTIFIC REPORTS, v. 6, NOV 22 2016. Web of Science Citations: 3.

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