Studies regarding knowledge organization and acquisition are of great importance to understand areas related to science and technology. A common way to model the relationship between different concepts is through complex networks. In such representations, networks' nodes store knowledge and edges represent their relationships. Several studies that considered this type of structure and knowledge acquisition dynamics employed one or more agents to discover node concepts by walking on the network. In this study, we investigate a different type of dynamics adopting a single node as the "network brain." Such a brain represents a range of real systems such as the information about the environment that is acquired by a person and is stored in the brain. To store the discovered information in a specific node, the agents walk on the network and return to the brain. We propose three different dynamics and test them on several network models and on a real system, which is formed by journal articles and their respective citations. The results revealed that, according to the adopted walking models, the efficiency of self-knowledge acquisition has only a weak dependency on topology and search strategy.
Authorship attribution is a natural language processing task that has been widely studied, often by considering small order statistics. In this paper, we explore a complex network approach to assign the authorship of texts based on their mesoscopic representation, in an attempt to capture the flow of the narrative. Indeed, as reported in this work, such an approach allowed the identification of the dominant narrative structure of the studied authors. This has been achieved due to the ability of the mesoscopic approach to take into account relationships between different, not necessarily adjacent, parts of the text, which is able to capture the story flow. The potential of the proposed approach has been illustrated through principal component analysis, a comparison with the chance baseline method, and network visualization. Such visualizations reveal individual characteristics of the authors, which can be understood as a kind of calligraphy.
Gostaria de agradecer a todos que fizeram parte dessa minha jornada acadêmica e pessoal que culmina nessa dissertação. Seria um desserviço tentar listar todos que tiveram um impacto significativo nessa minha jornada, sejam amigos que trago desde a graduação, ou até mesmo antes, que compartilharam as diversas etapas da vida dividindo as mesmas angústias e alívios e alegrias, colegas que sempre me ajudaram com ideias e descobertas e aprimoramentos, os grupos de confraria, jogatinas e RPGs que propiciaram momentos extraordinários ou professores que me deram instrução e direcionamento fundamental para eu ser quem sou hoje. Reservo um agradecimento especial aos meus pais por sempre estarem ao meu lado me apoiando e acreditando em mim. Agradeço também à Capes pelo financiamento dos meus estudo.
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