Difusão orientada por centralidade em redes complexas dinâmicas

Abstract

The dynamics is a characteristic present in many real systems, such as communication networks, social, biological and technological. Diffusion processes in complex networks may arise, for example, search data, routing data and the spread of diseases. Thus, understanding the time required for diffusion is an important topic of study in dynamic complex networks. This dissertation is a study of how centrality measures can help in reducing the time information dissemination in dynamic complex networks. Using data from synthetic and real systems is shown that if the dynamics is disregarded, the time needed for spreading an information network is underestimated. Diffusion algorithms have been proposed that consider metrics of centrality in graphs. Finally, we analyze the impact of a simple model for predicting edge algorithms in diffusion based on centrality that have been proposed in this dissertation.