Characterization, analysis, simulation and classification of complex networks
Use of machine learning as an indication of price movement trends in the financial...
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| Grant number: | 15/08003-4 |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| Start date: | November 01, 2015 |
| End date: | November 01, 2018 |
| Field of knowledge: | Interdisciplinary Subjects |
| Principal Investigator: | Luciano da Fontoura Costa |
| Grantee: | Filipi Nascimento Silva |
| Host Institution: | Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Associated research grant: | 11/50761-2 - Models and methods of e-Science for life and agricultural sciences, AP.TEM |
| Associated scholarship(s): | 17/09280-7 - Probing the structure and dynamics of information networks, BE.EP.PD |
Abstract A new perspective of making science is being adopted by many researchers over the world. Referred to as e-Science, this new approach to science conveys several modern paradigms, including interdisciplinary techniques to solve complex and large real problems, methods to effectively storage and analyze large datasets, as well as ways to understand and assess the structure of science itself. It has been used to organize, visualize and summarize data and scientific discoveries for a wide range of complex systems. The dynamics in such systems strongly rely on the intricate patterns of relationships between its elements, which in turn undermine the effectiveness of the use of reductionistic approaches (i.e. where systems are fragmented so that each part can be analyzed separately and later combined to understand the whole). A natural way to understand these systems is through the use of methods employed from network science, however, because many of them also present dynamical features and structure, there is an urge to develop new tools and methods to model and analyze dynamical networks, which are quite novel in the literature. In this project, we will explore a diverse set of problems involving real complex systems shared by e-Science field. This will be done by modeling and representing these systems as dynamical complex networks, as well as by creating new or extending techniques of complex networks analysis. Because of the interdisciplinary nature of this project, many distinct problems will be studied, including: modeling of the financial market network; characterization of biological structures and gene expression data; automated summarization of scientific data and historical events; and knowledge modeling. However, the analysis will be conducted by using a core set of methods, including visualization techniques, natural language processing and novel network based approaches, such as mapping network to the topological feature space, modeling and analysis of crisis/disease dynamics, stochastic dynamics, etc. We expect that the results revealed along the realization of this project will contribute to motivate others researchers from a wide range of scientific fields to incorporate our framework in their own pipelines. (AU) | |
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