skip to main content
Visitante
Meu Espaço
Minha Conta
Sair
Identificação
This feature requires javascript
Tags
Revistas Eletrônicas (eJournals)
Livros Eletrônicos (eBooks)
Bases de Dados
Bibliotecas USP
Ajuda
Ajuda
Idioma:
Inglês
Espanhol
Português
This feature required javascript
This feature requires javascript
Primo Search
Busca Geral
Busca Geral
Acervo Físico
Acervo Físico
Produção Intelectual da USP
Produção USP
Search For:
Clear Search Box
Search in:
Busca Geral
Or select another collection:
Search in:
Busca Geral
Busca Avançada
Busca por Índices
This feature requires javascript
This feature requires javascript
Structure characterization of complex networks for machine learning
Anghinoni, Leandro
Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Ciências Matemáticas e de Computação 2023-07-03
Acesso online
Exibir Online
Detalhes
Resenhas & Tags
Mais Opções
This feature requires javascript
Enviar para
Adicionar ao Meu Espaço
Remover do Meu Espaço
E-mail (máximo 30 registros por vez)
Imprimir
Link permanente
Referência
EasyBib
EndNote
RefWorks
del.icio.us
Exportar RIS
Exportar BibTeX
This feature requires javascript
Título:
Structure characterization of complex networks for machine learning
Autor:
Anghinoni, Leandro
Orientador:
Liang, Zhao; Silva, Israel Tojal da
Assuntos:
Aprendizado De Máquina
;
Redes Complexas
;
Graph Neural Network
;
Redes Core-Periphery
;
Estrutura De Comunidades
;
Core- Periphery Network
;
Complex Networks
;
Machine Learning
;
Community Structure
Notas:
Tese (Doutorado)
Descrição:
Over the last decade, machine learning has flourished due to significant advances in hardware capacity and model developments. Network based models have recently gained a lot of attention due to their capacity to learn not only from the physical features (similarity, distribution, etc.), but also from the connectivity pattern of the data. In the search of better models, the research has evolved to incorporate the structure of the network in the learning process. Some recent works have shown that exploiting the network structure can lead to better learning performance. This is done by capturing the more relevant connections in the training process based on the network topology. In light of this, this thesis carries out four studies to incorporate the network structure in machine learning algorithms. In the first study, the network structure is used to learn time series patterns via community detection algorithms. The second study uses a core-periphery network structure to represent data where the data within one of the classes has a very high dispersion and is hard to be classified by traditional algorithms. In other words, we introduce a network-based method to represent data pattern of the data without pattern. The third study aims to model an epidemic outbreak via link prediction in a network constructed from real data. We find that social isolation and wearing masks can effectively decrease the COVID-19 epidemics peak. In the final study, we propose a novel Graph Neural Network (GNN) model by combining the community structure of the underlying data graph and the feature vectors of the nodes to generate a graph embedding in a fast way. The proposed GNN can avoid the over-smoothing drawback of classic ones. These studies show that complex network approach can overcome various shortcomings of classic learning techniques.
DOI:
10.11606/T.55.2023.tde-13092023-143213
Editor:
Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Ciências Matemáticas e de Computação
Data de criação/publicação:
2023-07-03
Formato:
Adobe PDF
Idioma:
Inglês
Links
Este item no Dedalus
Teses e Dissertações USP
Acesso ao doi
E-mail do orientador
E-mail do coorientador
This feature requires javascript
This feature requires javascript
Voltar para lista de resultados
This feature requires javascript
This feature requires javascript
Buscando em bases de dados remotas. Favor aguardar.
Buscando por
em
scope:(USP_PRODUCAO),scope:(USP_EBOOKS),scope:("PRIMO"),scope:(USP),scope:(USP_EREVISTAS),scope:(USP_FISICO),primo_central_multiple_fe
Mostrar o que foi encontrado até o momento
This feature requires javascript
This feature requires javascript