Francisco Aparecido Rodrigues

• Abstract

  The project aims to strengthen academic and scientific integration between two of the main research groups in Complex Systems in Brazil, based at ICMC-USP and UFV, promoting the training of highly qualified human resources and the internationalization of research activities. The proposal includes the development of frontier studies in the field of spreading processes on complex networks, …

  The Next Era of Spreading Processes on Complex Networks: Higher-Order Interactions, Temporality, and Machine Learning, AV.BR

• Abstract

  The Applied Research Center for in Artificial Intelligence, IARA, is formed by a multidisciplinary team, with national experts of internationally recognized for their competence in artificial intelligence, internet of things telecommunications and smart cities. The center, hosted in São Carlos, in the state of São Paulo, will work in network with researchers from all Brazilian regions, or…

  IARA - Artificial Intelligence in the Remaking of Urban Environments, AP.PCPE

• Abstract

  The focus of this proposal is the transfer of mathematical knowledge to other areas of science, technology and industry, through a structured research center specially tailored for this purpose. All mathematical knowledge is ultimately applicable, if not directly, through other areas of knowledge. In some areas the application of mathematics is almost immediate. However, the practical imp…

  CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

• Abstract

  Complex networks serve as a representation of the intricate structures found in complex systems, ranging from biological interactions to social connections. The study of these networks has gained significant importance in recent decades, enabling the modelling of organizational structures and dynamic processes in various systems. Over the past years, there has been a growing interest in a…

  Predictive modeling of complex network structure and dynamics using machine learning methods, AV.EXT

• Abstract

  Complex systems are made of discrete parts that interact in a non-linear way. The Internet, social networks and ecosystems are examples of complex systems. A big challenge in this area is the prediction of the dynamical processes, such as epidemic spreading and synchronization of coupled oscillators, from the network structure. In the present project, we aim to perform the prediction and …

  Prediction and inference in complex systems, AP.R

• Abstract

  In this Project we pretend to introduce and characterize null models, based on random matrix ensembles, for complex networks of current interest. In particular we plan to study: (i) random networks with gain and/or loss, (ii) non-uniform random regular graphs, (iii) bipartite networks, (iv) mutualistic networks, and (v) directed networks. We will perform a scaling analysis to define the u…

  Random matrix theory approach to complex networks, AV.EXT

(Only some records are available in English at this moment)

• Abstract

  This research project aims to investigate how the structural characteristics of the initial spreader node affect the dynamics and outcomes of rumor propagation in complex, non-homogeneous networks. Rumor spreading is modeled as a stochastic process unfolding over directed graphs, where nodes represent individuals and edges capture social interactions. Drawing upon established models from …

  Machine learning of rumour spreading in complex networks, BP.IC

• Abstract

  The existence of brain patterns that reflect an individual brain print is a recent approach in neuroscience and has been investigated using different dataset and different analysis methods. However, all these studies have assessed individuals in waking state, i.e. even over different days and performing different tasks, researchers has been observed that the physiological brain pattern ha…

  Investigating Brain Signatures in Altered States of Consciousness with Dynamic Networks - A Neurophysiological and Cellular Approach, BP.PD

• Abstract

  This research project proposes a comprehensive and innovative investigation into the robustness and resilience of complex networks, areas of growing importance in the study of complex systems, with applications in transportation, data, and power networks. The objective is to develop theoretical and computational methods to analyze and enhance the robustness and resilience of these network…

  Robustness of Complex Networks: An Approach Using Machine Learning Methods, BP.PD

(Only some records are available in English at this moment)

• Abstract

  Graph embeddings have emerged as powerful tools for representing complex networks in low-dimensional vector spaces while preserving essential topological properties. This project focuses on evaluating and comparing various graph embedding methods in their ability to maintain community structures-subsets of nodes with higher internal connectivity than external. While popular methods such a…

  Graph Embedding for Community Preservation, BP.IC

• Abstract

  As an effect of climate change, we are increasingly witnessing the occurrence of extreme weather events such as hydrological anomalies, fierce cyclones and heat waves. The socioeconomic impact of such phenomena is immense and there is great scientific interest in understanding their behavior, characterization and forecast, exceptionally challenging tasks considering their extreme behavior…

  Causal inference and machine learning on complex climatic systems, BP.MS

• Abstract

  The study of opinion dynamics is extremely relevant for understanding the emergence of the polarization phenomenon in social networks. This project aims to use machine learning to predict with high accuracy dynamic variables associated with the Sznajd model in complex networks. We will consider modeling through complex networks, Monte Carlo simulations, and regression methods. The project…

  Machine learning-based prediction of Sznajd model in complex networks, BP.IC

(Only some records are available in English at this moment)

• Abstract

  In this project, we intend to explore the application of recurrence-based methods for climate data, specifically focusing on detecting relevant conditions and parameters associated with tipping points and extreme events mainly driven by climate change. By integrating insights from nonlinear dynamics with machine learning techniques, we aim to uncover hidden patterns and dynamical anomalie…

  Dynamical Anomalies in Complex Systems: Application in Climate Science, BE.EP.PD

• Abstract

  Mathematical modeling is essential for understanding spreading processes in complex networks. While much research has focused on static networks, the importance of temporal networks is becoming increasingly recognized. In this project, we aim to develop a novel approach to modeling temporal networks using mobile agents and a movement structure that allows for the creation of dynamic clust…

  Exploring the impact of multilayer networks on dynamic processes in temporal networks, BE.EP.MS

• Abstract

  The interest in learning how rumors spreads in society has led researches to develop theoretical computational models to investigate the underlying mechanisms behind the spread of fake news in our society. Currently, literature on the topic provides a variety of epidemiological and cultural models. While each type of modeling adopts a different approach, all of them aim to resemble some a…

  Machine learning of social dynamics in complex networks, BE.EP.DR

• Abstract

  Incidence of infectious diseases as well as the costs and benefits of public health interventions are not evenly distributed across society. In this project, we aim to analyze the geographic and socioeconomic differences in infectious disease incidence and antibiotic prescribing, and measure their respective contributions to the overall variation in antibiotic prescriptions. We also inten…

  Geographic and socioeconomic heterogeneity in the incidence and treatments of infectious diseases, BE.EP.DR

(Only some records are available in English at this moment)