An analysis of network automata as models for biological and natural processes

• Grant number: 21/08325-2
• Support Opportunities: Regular Research Grants
• Start date: January 01, 2022
• End date: October 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computational Mathematics
• Agreement: Research Foundation - Flanders (FWO)
• Principal Investigator: Odemir Martinez Bruno
• Grantee: Odemir Martinez Bruno
• Principal researcher abroad: Jan Marcel Baetens
• Institution abroad: Ghent University (UGent), Belgium
• Principal researcher abroad: Luis Enrique Correa da Rocha
• Institution abroad: Ghent University (UGent), Belgium
• Host Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 23/07241-5 - Combining network-automata and neural networks for data analysis, BP.PD

Abstract

Increasing computational power allows for the description and prediction of natural processes by modelling its smallest-scale interactions, recreating natural phenomena in a bottom-up fashion. Two classes of such approaches, which are especially pertinent for modelling spatio-temporal processes, are cellular automata (CAs) and their topological extension, network automata (NAs). The simplest types of CAs and NAs are well studied, but for these models to acquire the status of a strong scientific paradigm, they must allow for simple model extensions, whilst at the same time remaining methodologically robust. We will extend these computational models by introducing "spatial heterogeneity", which includes "rule heterogeneity" and "topological heterogeneity". The former translates to extending the model in such a way that the set of rules determining the microscopic dynamics, and therefore the emergent process, must no longer be the same on every spatially distinct region. The latter, which is exclusive to NAs, translates to allowing different types of connections between nodes. In both cases, a mathematical framework for these heterogeneities as well as a methodological enquiry into their effect on model stability, quantified by discrete extensions of the Lyapunov exponent, are at the core of this proposal. In a final stage, we will mobilize these models and the newly developed analytical techniques in two applications: epidemiology and opinion dynamics. (AU)

Scientific publications

(11)

(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)

VALLE, JOAO; BRUNO, ODEMIR M.. Dynamics and patterns of the least significant digits of the infinite-arithmetic precision logistic map orbits. CHAOS SOLITONS & FRACTALS, v. 180, p. 9-pg., 2024-01-24. (21/08325-2, 22/01935-2, 18/22214-6)

NEIVA, MARIANE B.; BRUNO, ODEMIR M.. Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v. 626, p. 11-pg., 2023-08-14. (18/22214-6, 21/08325-2)

ZIELINSKI, KALLIL M.; SCABINI, LEONARDO; RIBAS, LUCAS C.; DA SILVA, NUBIA R.; BEECKMAN, HANS; VERWAEREN, JAN; BRUNO, ODEMIR M.; DE BAETS, BERNARD. Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 231, p. 12-pg., 2025-01-20. (21/09163-6, 23/10442-2, 21/08325-2, 18/22214-6, 22/03668-1, 23/04583-2)

VALLE, JOAO; BRUNO, ODEMIR MARTINEZ. Forecasting chaotic time series: Comparative performance of LSTM-based and Transformer-based neural network. CHAOS SOLITONS & FRACTALS, v. 192, p. 9-pg., 2025-03-01. (21/08325-2, 22/01935-2, 18/22214-6)

VALLE, JOAO; MACHICAO, JEANETH; BRUNO, ODEMIR M.. Chaotical PRNG based on composition of logistic and tent maps using deep-zoom. CHAOS SOLITONS & FRACTALS, v. 161, p. 10-pg., 2022-06-27. (21/07377-9, 21/08325-2, 18/22214-6, 22/01935-2, 20/03514-9)

SCABINI, LEONARDO F. S.; BRUNO, ODEMIR M.. Structure and performance of fully connected neural networks: Emerging complex network properties. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, v. 615, p. 17-pg., 2023-03-03. (21/08325-2, 19/07811-0)

SCABINI, LEONARDO; ZIELINSKI, KALLIL M.; RIBAS, LUCAS C.; GONCALVES, WESLEY N.; DE BAETS, BERNARD; BRUNO, ODEMIR M.. RADAM: Texture recognition through randomized aggregated encoding of deep activation maps. PATTERN RECOGNITION, v. 143, p. 13-pg., 2023-11-01. (22/03668-1, 21/09163-6, 18/22214-6, 21/07289-2, 21/08325-2, 19/07811-0)

ZIELINSKI, KALLIL M. C.; SCABINI, LEONARDO; RIBAS, LUCAS C.; BRUNO, ODEMIR M.. Exploring neighborhood variancy for rule search optimization in Life-like Network Automata. 2024 14TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION SYSTEMS, ICPRS, v. N/A, p. 7-pg., 2024-01-01. (23/04583-2, 23/10442-2, 21/08325-2, 24/00530-4, 18/22214-6)

ZIELINSKI, KALLIL M. C.; RIBAS, LUCAS C.; MACHICAO, JEANETH; BRUNO, ODEMIR M.. A network classification method based on density time evolution patterns extracted from network automata. PATTERN RECOGNITION, v. 146, p. 10-pg., 2023-09-29. (23/04583-2, 21/07289-2, 21/08325-2, 20/03514-9, 22/03668-1, 18/22214-6)

MERENDA, JOAO, V; TRAVIESO, GONZALO; BRUNO, ODEMIR M.. Pattern recognition on networks using bifurcated deterministic self-avoiding walks. CHAOS SOLITONS & FRACTALS, v. 194, p. 10-pg., 2025-02-20. (21/08325-2, 18/22214-6)

SCABINI, LEONARDO; DE BAETS, BERNARD; BRUNO, ODEMIR M.. Improving deep neural network random initialization through neuronal rewiring. Neurocomputing, v. 599, p. 13-pg., 2024-07-17. (19/07811-0, 21/09163-6, 21/08325-2, 18/22214-6)