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Characterization of time-varying complex networks

Grant number: 16/23642-6
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): September 01, 2017
Effective date (End): July 31, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Marcos Gonçalves Quiles
Grantee:Alessandra Marli Maria Morais Gouvêa
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Associated research grant:15/50122-0 - Dynamic phenomena in complex networks: basics and applications, AP.TEM


The study of systems through complex networks has shown to be a plenty active area. Such study is commonly carried out based on measures able to characterize the main topological attributes of the network. Knowing the network topology structure is fundamental to understand and recognize of real systems, as well as to model and simulate synthetic ones. Most of the researchers have focused on the study of networks with fixed topology (static). However, real systems are not necessarily static. In the dynamic scenario, the topological structure may vary. Thus, developing measures which are able to characterize the structural properties of dynamical networks, and their respective changes over time, are essential in many scenarios. Currently, some authors have dealt with networks whose topology changes over time. Nevertheless, the deficit of theories and methods in this research field is still evident. In this context, this project aims to propose new measures to characterize dynamic networks in both micro and macro scale. The initial approach will consider the dynamic community detection model proposed by Quiles et al. as the base model. Specifically, we will conduct the investigation in two critical aspects. 1) Investigating new interaction functions to permit the application of the model in general and dynamic networks, and 2) scrutinizing the particle space proposed by Quiles et al. as an alternative graph representation to compute new measures to dynamic networks.