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Master's Dissertation
DOI
https://doi.org/10.11606/D.104.2019.tde-29082019-150859
Document
Author
Full name
Andrey Luan Gomes Contel
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2019
Supervisor
Committee
Rodrigues, Francisco Aparecido (President)
Boas, Paulino Ribeiro Villas
Silva, Paulo Henrique Ferreira da
Title in Portuguese
Inferência estatística e amostragem de redes complexas
Keywords in Portuguese
Amostragem
Aprendizado de máquina
Grafos
Inferência
Redes complexas
Abstract in Portuguese
Redes complexas são formadas por amostras de dados obtidos a partir do mapeamento da estrutura de sistemas complexos. Geralmente, diferentes métodos de amostragem são considerados para a construção da rede. No entanto, dependendo do método, as amostras podem ser muito diferentes das redes originais. Logo, uma comparação entre os diferentes métodos de amostragem é altamente recomendável, de modo a permitir escolher o método que preserve uma determinada característica. Nesse trabalho, propomos uma comparação de métodos de amostragem de redes e um estudo considerando métodos inferência estatística e técnicas de amostragem em grafos para estimar as principais medidas de caracterização.
Title in English
Statistical inference and sampling of complex network
Keywords in English
Complex network
Graphs
Inference
Machine learning
Sampling
Abstract in English
Complex networks are formed by data samples obtained from the mapping of complex system structures. Generally, different sampling methods are considered for the construction of the network. However, depending on the method, the samples may be very different from the original networks. Therefore, a comparison between the different methods of sampling is highly recommended, in order to allow to choose the method that preserves a certain characteristic. In this work, we propose a comparison of network sampling methods and a study considering statistical inference methods and graph sampling techniques to estimate the main characterization measures.
 
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Publishing Date
2019-10-18
 
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