Complex network component unfolding using a particle competition technique

• Ano de defesa: 2017
• Autor(a) principal: Urio, Paulo Roberto
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Agrupamento de dados; Aprendizado de máquina; Aprendizado semissupervisionado; Community detection; Complex networks; Data clustering; Detecção de comunidades; Machine learning; Redes complexas; Semi-supervised learning
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14092017-091318/

This work applies complex network theory to the problem of semi-supervised and unsupervised learning in networks that are representations of multivariate datasets. Complex networks allow the use of nonlinear dynamical systems to represent behaviors according to the connectivity patterns of networks. Inspired by behavior observed in nature, such as competition for limited resources, dynamical system models can be employed to uncover the organizational structure of a network. In this dissertation, we develop a technique for classifying data represented as interaction networks. As part of the technique, we model a dynamical system inspired by the biological dynamics of resource competition. So far, similar methods have focused on vertices as the resource of competition. We introduce edges as the resource of competition. In doing so, the connectivity pattern of a network might be used not only in the dynamical system simulation but in the learning task as well.