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Prediction and inference in complex systems


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 inference of dynamical processes in networks. We will consider methods of Bayesian inference to determine which network properties most influence dynamical processes simulated on the network structure. Machine learning approaches will be adopted to perform the prediction of dynamical processes, including epidemic spreading and synchronization. Applications, such as medicine, epidemiology and link prediction, will also be studied in this project. The goals proposed here will open a new field that considers machine learning and statistics to model and predict complex systems. (AU)

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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)
ALEXANDRE, MICHEL; DE MORAES, KAUE L.; RODRIGUES, FRANCISCO A.. isk-dependent centrality in the Brazilian stock marke. JOURNAL OF COMPLEX NETWORKS, v. 10, n. 1, . (19/23293-0)
VENTURA, PAULO CESAR; MORENO, YAMIR; RODRIGUES, FRANCISCO A.. Role of time scale in the spreading of asymmetrically interacting diseases. PHYSICAL REVIEW RESEARCH, v. 3, n. 1, . (16/24555-0, 13/07375-0, 19/11183-5, 19/23293-0)
ALEXANDRE, MICHEL; SILVA, THIAGO CHRISTIANO; CONNAUGHTON, COLM; RODRIGUES, FRANCISCO A.. The drivers of systemic risk in financial networks: a data-driven machine learning analysis. CHAOS SOLITONS & FRACTALS, v. 153, n. 1, . (19/23293-0)

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