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Complex network component unfolding using a particle competition technique

Urio, Paulo Roberto

Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Ciências Matemáticas e de Computação 2017-06-12

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  • Título:
    Complex network component unfolding using a particle competition technique
  • Autor: Urio, Paulo Roberto
  • Orientador: Liang, Zhao
  • Assuntos: Agrupamento De Dados; Aprendizado De Máquina; Aprendizado Semissupervisionado; Redes Complexas; Detecção De Comunidades; Machine Learning; Data Clustering; Complex Networks; Community Detection; Semi-Supervised Learning
  • Notas: Dissertação (Mestrado)
  • Descrição: 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.
  • DOI: 10.11606/D.55.2017.tde-14092017-091318
  • Editor: Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Ciências Matemáticas e de Computação
  • Data de criação/publicação: 2017-06-12
  • Formato: Adobe PDF
  • Idioma: Inglês

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