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Transformations of complex networks and their implication in topology and dynamics of complex systems

Grant number: 18/10489-0
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): February 01, 2019
Effective date (End): January 31, 2021
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Luciano da Fontoura Costa
Grantee:Henrique Ferraz de Arruda
Home Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:15/22308-2 - Intermediate representations in Computational Science for knowledge discovery, AP.TEM

Abstract

Many types of systems can be represented as complex networks, from biological structures, such as neuronal networks, to systems that were created by the human beings, such as power grid networks. Distinct respective interpretations and measurements were proposed in the literature. As an alternative and a complement of the already used methods, this project proposes a better exploration and comprehension of such networks through transformations in their respective topologies. These alterations can be done by adding or deleting edges in the network, by following some given criteria. Note that elimination of edges can be used to eliminate noise. Among the possibilities to modify the network topology, we intend to use topological information, such as dilatations ('L-Percolations') and centrality measurements. From the proposed representations, network characteristics will be explored. For example, it would be interesting to study the impact of the new topology in the community structure of the network to understand if the modifications strengthen or weaken the modules. Another characteristic that shall be explored is the relationship between alterations in the network topology and their respective consequences in the dynamics. Furthermore, from the knowledge obtained by the topological structure and measures provided by the dynamics, new information can be obtained to characterize and classify the networks. As a complement of the proposed measurements and analysis, we will consider our methodology for link prediction.