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Characterizing time-varying networks: methods and applications

Grant number: 16/16291-2
Support Opportunities:Scholarships abroad - Research
Effective date (Start): January 01, 2017
Effective date (End): December 31, 2017
Field of knowledge:Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
Principal Investigator:Marcos Gonçalves Quiles
Grantee:Marcos Gonçalves Quiles
Host Investigator: Richard Charles Wilson
Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Research place: University of York, England  
Associated research grant:15/50122-0 - Dynamic phenomena in complex networks: basics and applications, AP.TEM


The study of real systems via complex networks has been a very active area in recent years. However, much of the progress made during this period is related to the study of the properties of networks with fixed topology (static). Commonly, the study and synthesis of complex networks make use of measures to characterize its main topological attributes. The characterization of the network is useful both in understanding and recognition of real systems and in modeling and simulation of synthetic systems. In the dynamic scenario, the topological structure may vary over time and may even result in the change of system functions and properties under study. Thus, the definition of measures to characterize the structural properties of dynamic networks and their changes over time are essential in many non-stationary scenarios, i.e. the climate.In this context, this research project has as one of its objectives to develop novel approaches to characterize time-varying networks both in micro and in macro scale. In particular, our aim is to extend the model proposed by Quiles et al. [1] to detect community structure in generic networks, such as weighted, directed, and multilayer networks with lower computational complexity. Further, we will scrutinize the particle space proposed in [1] as an alternative representation to compute new characterization measurements. In this research project, we will also investigate the application of the methods for characterizing complex time-varying networks to study the evolution of climate networks. As an initial approach, we will extend the methodologies proposed in [2,3] to examine the evolution of the community structure in climate networks generated from satellite images. Finally, it is worth noting that the results achieved in research will directly contribute to the Thematic Project FAPESP/DFG (Proc. 2015 / 50122-0). The proponent coordinates the subproject C6 which aims to study systematically, via network analysis, the dynamics of the monsoon system South America, particularly the influence of tropical and extra-tropical systems on monsoon system.----Project C6: Complex network analysis of scale-dependent and cross-scale interactions tropical between and extra-tropical climate modes. (AU)

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Scientific publications (7)
(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)
GOUVEA, ALESSANDRA M. M. M.; DA SILVA, TIAGO S.; MACAU, ELBERT E. N.; QUILES, MARCOS G.. Force-directed algorithms as a tool to support community detection. European Physical Journal-Special Topics, . (19/00157-3, 17/05831-9, 15/50122-0, 19/26283-5, 16/16291-2, 16/23698-1, 16/23642-6)
FERREIRA, LEONARDO N.; VEGA-OLIVEROS, DIDIER A.; COTACALLAPA, MOSHE; CARDOSO, MANOEL F.; QUILES, MARCOS G.; ZHAO, LIANG; MACAU, ELBERT E. N.. Spatiotemporal data analysis with chronological networks. NATURE COMMUNICATIONS, v. 11, n. 1, . (13/07375-0, 17/05831-9, 18/24260-5, 16/23698-1, 16/16291-2, 15/50122-0, 19/00157-3, 19/26283-5)
CERON, WILSON; SANTOS, LEONARDO B. L.; NETO, GIOVANNI DOLIF; QUILES, MARCOS G.; CANDIDO, ONOFRE A.. Community Detection in Very High-Resolution Meteorological Networks. IEEE Geoscience and Remote Sensing Letters, v. 17, n. 11, p. 2007-2010, . (16/16291-2, 15/50122-0, 18/06205-7)
GOUVEA, ALESSANDRA M. M. M.; VEGA-OLIVEROS, DIDIER A.; COTACALLAPA, MOSHE; FERREIRA, LEONARDO N.; MACAU, ELBERT E. N.; QUILES, MARCOS G.; GERVASI, O; MURGANTE, B; MISRA, S; GARAU, C; et al. Dynamic Community Detection into Analyzing of Wildfires Events. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT I, v. 12249, p. 16-pg., . (16/16291-2, 19/26283-5, 17/05831-9, 19/00157-3, 15/50122-0, 16/23698-1, 16/23642-6)
GOUVEA, ALESSANDRA M. M. M.; RUBIDO, NICOLAS; MACAU, ELBERT E. N.; QUILES, MARCOS G.. Importance of Numerical Implementation and Clustering Analysis in Force-Directed Algorithms for Accurate Community Detection. Applied Mathematics and Computation, v. 431, p. 21-pg., . (19/26283-5, 17/05831-9, 16/23698-1, 16/16291-2, 19/00157-3)
GOUVEA, ALESSANDRA M. M. M.; DA SILVA, TIAGO S.; MACAU, ELBERT E. N.; QUILES, MARCOS G.. Force-directed algorithms as a tool to support community detection. European Physical Journal-Special Topics, v. 230, n. 14-15, p. 2745-2763, . (16/16291-2, 19/26283-5, 16/23698-1, 17/05831-9, 19/00157-3, 16/23642-6, 15/50122-0)
COTACALLAPA, MOSHE; BERTON, LILIAN; FERREIRA, LEONARDO N.; QUILES, MARCOS G.; ZHAO, LIANG; MACAU, ELBERT E. N.; VEGA-OLIVEROS, DIDIER A.; IEEE. Measuring the engagement level in encrypted group conversations by using temporal networks. 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., . (18/24260-5, 16/16291-2, 16/23698-1, 19/00157-3, 17/05831-9, 18/01722-3, 15/50122-0)

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