Visual analytics of heterogeneous brain networks using multilevel methods
Using semantical information to classify texts modelled as complex networks
Multi-level Multi-source visual analysis of government text data
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| Grant number: | 17/09280-7 |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |
| Start date: | July 01, 2017 |
| End date: | June 30, 2018 |
| Field of knowledge: | Interdisciplinary Subjects |
| Principal Investigator: | Luciano da Fontoura Costa |
| Grantee: | Filipi Nascimento Silva |
| Supervisor: | Filippo Menczer |
| Host Institution: | Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Institution abroad: | 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. | |
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