- Docentes: João Paulo Góis.
- Agência Financiadora: Capes (Print).
Computer Science is increasingly present in many fields of knowledge, fostering the need to create new technologies to deal with complex problems in different areas of scientific research. Such technological advances are possible through theoretical results that support the generation of computational models adapted to new interdisciplinary problems. For example, the study of combinatorial structures has a fundamental role in the development of efficient algorithms to solve problems in Bioinformatics, Scientometry, and Computer Graphics. In particular, analysis of complex networks, which are graphs with non-trivial topological characteristics that occur in many real-world situations, is a research area with several uses and public
interest, from social behavior in networks to biological functioning of neuronal networks, genes, and proteins.
This research project has two main goals:
1) to investigate structural, combinatorial, and algorithmic properties of graphs and related discrete structures;
2) to apply combinatorial techniques to obtain advances in Bioinformatics, Scientometry, and Interactive Segmentation of Images and Videos.
In Bioinformatics, the focus will be on inference, modeling, and simulation of molecular biology networks, using analysis of complex networks, including the development of methods for detecting communities and repeating local structural patterns usually associated with important functions in the network. In Scientometrics, the focus is on the application of concepts of graph theory and complex networks for the analysis of academic research networks, which also involves community search, local structural patterns in graphs, and prediction of future connections. Finally, in Computer Graphics the objective is to segment images and videos through the application of Laplacian graphs built from the inputs to the problem of segmentation or co-segmentation. Although the focus of applications is mainly on the three fronts mentioned above, practically all areas of science depend on combinatorial analysis, most notably: physics, chemistry, biology, engineering, astronomy, and social sciences (especially social network analysis).