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Probing the structure and dynamics of information networks

Grant number: 17/09280-7
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): July 01, 2017
Effective date (End): June 30, 2018
Field of knowledge:Interdisciplinary Subjects
Principal Investigator:Luciano da Fontoura Costa
Grantee:Filipi Nascimento Silva
Supervisor abroad: Filippo Menczer
Home Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Local de pesquisa : Indiana University, United States  
Associated to the scholarship:15/08003-4 - Complex network approach to e-Science and dynamic datasets, BP.PD

Abstract

Representing and modeling information has become a pivotal part of modern science. Most of the interesting problems today originates from complex systems that are not isolable neither dependent solely on a single discipline. In fact, for this kind of problems, it has become hard to even guess where a discipline ends and another starts. A contributing factor to this trend is the need of methods to analyze, characterize, organize and visualize information. Such tasks now span along several disciplines, including physics, statistics and computer science. In such a complex environment, network science emerged as a suitable and general representation for a myriad of real-world systems. This includes representing information itself in terms of its intricate relationships. However, since there is still a lack of such techniques, probing their structure and dynamics is still a challenging problem. In this proposal, we intent to continue the research on understanding the structure and dynamics of complex networks, currently being undertaken by the candidate, but now directing the focus of analysis to information networks. For this, we propose the development and use of a framework to investigate dynamics on such structures by integrating data mining and machine learning approaches with network analysis. This may includes new visualization techniques, the use of natural language processing and new network-based approaches, such as mapping network dynamics to a feature space and modeling their evolution in terms of communities. Finally, we plan to illustrate the proposed framework through many applications, such as the analysis of social media data and of the structure and dynamics of science itself.

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)
SILVA, FILIPI N.; COMIN, CESAR H.; COSTA, LUCIANO DA F. Malleability of complex networks. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, AUG 2019. Web of Science Citations: 0.
DOMINGUES, G. S.; SILVA, F. N.; COMIN, C. H.; COSTA, L. DA F. Topological characterization of world cities. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, AUG 2018. Web of Science Citations: 2.
LIMA, THALES S.; DE ARRUDA, HENRIQUE F.; SILVA, FILIPI N.; COMIN, CESAR H.; AMANCIO, DIEGO R.; COSTA, LUCIANO DA F. The dynamics of knowledge acquisition via self-learning in complex networks. Chaos, v. 28, n. 8 AUG 2018. Web of Science Citations: 2.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.