Avaliação distribuída de centralidade em redes complexas

• Ano de defesa: 2012
• Autor(a) principal: Wehmuth, Klaus
• Orientador(a): Ziviani, Artur
• Banca de defesa: Gomes, Antonio Tadeu Azevedo , Silva, Ana Paula Couto da , Figueiredo, Daniel Ratton
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Laboratório Nacional de Computação Científica
• Programa de Pós-Graduação: Programa de Pós-Graduação em Modelagem Computacional
• Departamento: Serviço de Análise e Apoio a Formação de Recursos Humanos
• País: BR
• Palavras-chave em Português: Sistemas de computação; Redes de computação; Teoria dos grafos
• Palavras-chave em Inglês: Computer networks; Computer systems; Graph theory
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: https://tede.lncc.br/handle/tede/144

The last decade or so has witnessed an ever-increasing growth in the study of very large complex networks related to different areas, such as biology, sociology, and the Internet. In this broad context, the concept of centrality offers a measure of the relative importance of nodes within a given complex network and is thus crucially important to network analysis. There are many different definitions of network centrality, which rank the relative importance of nodes using different criteria, depending on the targeted analysis. Among these, the traditional closeness centrality ranks the nodes by how close each node is to all other nodes in the network. In other words, the most central nodes according to a ranking based on closeness centrality are those best positioned for efficient diffusion processes, such as information or goods distribution as well as disease or rumor spreading, and so on. Nevertheless, computing closeness centrality in large complex networks is costly because it is necessary to determine the distance between all pairs of nodes in the network, thus requiring full knowledge of the network's topology. In centrality-based network analysis, the position of each node in the centrality ranking is typically more important than the particular centrality value associated to each node. Here, we present a fully distributed method capable of yielding different kinds of centrality, among them one which node ranking correlates strongly with the closeness centrality ranking, but being much cheaper than the traditional algorithm and not requiring full knowledge of the network's topology. Overall, our method is a simple yet efficient alternative for distributively determining the closeness centrality ranking, enabling a centrality-based analysis of large scale-free complex networks.