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Spatiotemporal data analytics based on complex networks

Grant number: 18/24260-5
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): February 01, 2019
Effective date (End): January 31, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Zhao Liang
Grantee:Didier Augusto Vega Oliveros
Supervisor abroad: Santo Fortunato
Home Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
Local de pesquisa : Indiana University, United States  
Associated to the scholarship:16/23698-1 - Dynamical processes in complex network based on machine learning, BP.PD


Complex network theory has helped to identify valuable information in many domains, where systems are complex with non-trivial connections and properties. In this way, understanding how the network structure impacts the dynamics and also how to infer the structure from these dynamics is of paramount importance to the area. In this project, we aim to develop methods to analyze the patterns of activity and the evolution of dynamic networks. The project includes the design, development, and testing of new techniques for mining temporal and dynamic networks and, the application of network analysis techniques to real-world problems. We will analyze and assess dynamic network patterns in climate data, which are geo and temporally tagged. We are proposing a set of spatiotemporal data analytic methods that, although tested on this climate data, could in principle extended to any temporal data set. Possible other data sets that we will explore are the diffusion of disinformation, voting network data, and dynamic opinion networks. As for grounding for the geo-temporal assessment, we aim to begin by applying and adapting traditional network measures including centrality measures, community detection algorithms, and graph metrics for assessing structure. We will conduct the analyses considering the theory of network sciences, machine learning, and dynamic networks, using artificial and real data sets, evaluating the methods of the literature and applying to real-world problems.

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)
VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; BERTON, LILIAN. Evaluating link prediction by diffusion processes in dynamic networks. SCIENTIFIC REPORTS, v. 9, JUL 25 2019. Web of Science Citations: 0.

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