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Master's Dissertation
DOI
10.11606/D.55.2018.tde-18102018-170343
Document
Author
Full name
Vinícius Ferreira da Silva
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2018
Supervisor
Committee
Lopes, Alneu de Andrade (President)
Chalco, Jesús Pascual Mena
Liang, Zhao
Quiles, Marcos Gonçalves
Title in Portuguese
Otimização multinível em predição de links
Keywords in Portuguese
Otimização multinível
Predição de links
Abstract in Portuguese
A predição de links em redes é uma tarefa com aplicações em diversos cenários. Com a automatização de processos, as redes sociais, redes tecnológicas e outras cresceram muito em número de vértices e arestas. Portanto, a utilização de preditores de links em redes com alta complexidade estrutural não é trivial, mesmo considerando algoritmos de baixa complexidade computacional. A grande quantidade de operações necessárias para que os preditores possam escolher quais arestas são promissoras torna o processo de considerar a rede toda inviável na maioria dos casos. As abordagens existentes enfrentam essa característica de diversas formas, sendo que as mais populares são as que limitam o conjunto de pares de vértices que serão considerados para existência de arestas promissoras. Este projeto aborda a criação de uma estratégia que utiliza otimização multinível para contrair as redes, executar os algoritmos de predição de links nas redes contraídas e projetar os resultados de predição para a rede original, para reduzir o número de operações necessárias à predição de links. Os resultados mostram que a abordagem consegue reduzir o tempo necessário para predição, apesar de perdas esperadas na qualidade na predição.
Title in English
Multilevel optimization for link prediction
Keywords in English
Link prediction
Multilevel optimization
Abstract in English
Link prediction in networks is a task with applications in several scenarios. With the automation of processes, social networks, technological networks, and others have grown considerably in the number of vertices and edges. Therefore, the creation of systems for link prediction in networks of high structural complexity is not a trivial process, even considering low-complexity algorithms. The large number of operations required for predicting which edges are promising makes the considering of the whole network impracticable in many cases. The existing approaches face this characteristic in several ways, and the most popular are those that limit the set of vertex pairs that will be considered for the existence of promising edges. This project addresses a strategy that uses multilevel optimization to coarse networks, execute prediction algorithms on coarsened networks and project the results back to the original network, in order to reduce the number of operations for link prediction. The experiments show that the approach can reduce the time despite some expected losses of accuracy.
 
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Publishing Date
2018-10-18
 
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