Modeling interacting social dynamics in complex networks

• Grant number: 19/22277-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: January 01, 2021
• End date: October 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Francisco Aparecido Rodrigues
• Grantee: Aruane Mello Pineda
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID
• Associated scholarship(s): 21/13843-2 - Machine learning of social dynamics in complex networks, BE.EP.DR

Abstract

In this project, we are interested in improving information propagation models in complex networks. In particular, we are interested in modeling rumored competition, where agents try to contain the spread of information. We aim to determine which vertices are the most suitable to slow down the spread of a rumor that may, for example, be associated with misinformation. In addition, we will develop a model in which information can generate contrary opinions. In this case, the individual receiving the information may change his mind with a certain probability. Such models will be simulated in complex networks and the mathematical analysis will be done via medium field approximation. Real network data, such as Twitter, will be collected to validate some of the results obtained, where information propagation between individuals will be monitored. Multilayer networks as well as mobile agent networks should be considered.

Scientific publications

(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)

ALVES, CAROLINE L.; CURY, RUBENS GISBERT; ROSTER, KIRSTIN; PINEDA, ARUANE M.; RODRIGUES, FRANCISCO A.; THIELEMANN, CHRISTIANE; CIBA, MANUEL. Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments. PLoS One, v. 17, n. 12, p. 26-pg., 2022-12-16. (19/22277-0, 19/23293-0, 19/26595-7)

ALVES, CAROLINE L.; TOUTAIN, THAISE G. L. DE O.; AGUIAR, PATRICIA DE CARVALHO; PINEDA, ARUANE M.; ROSTER, KIRSTIN; THIELEMANN, CHRISTIANE; PORTO, JOEL AUGUSTO MOURA; RODRIGUES, FRANCISCO A.. Diagnosis of autism spectrum disorder based on functional brain networks and machine learning. SCIENTIFIC REPORTS, v. 13, n. 1, p. 20-pg., 2023-05-18. (19/26595-7, 19/23293-0, 19/22277-0)

ALVES, CAROLINE L.; PINEDA, ARUANE M.; ROSTER, KIRSTIN; THIELEMANN, CHRISTIANE; RODRIGUES, FRANCISCO A.. EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia. JOURNAL OF PHYSICS-COMPLEXITY, v. 3, n. 2, p. 13-pg., 2022-06-01. (19/23293-0, 19/26595-7, 19/22277-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

PINEDA, Aruane Mello. Modeling interacting social dynamics in complex networks. 2023. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.