Complex networks have been used in several applications in the past few decades. Complex systems can be observed in applications such as transportation, energy systems, internet, biology, and logistics, among other possible *implementations*. In such structures, there may be agents exploring nodes and identifying new concepts and discovering the network; this kind of exploration is known as Knowledge Acquisition and it has been deeply researched for the past decades. When exploring a network, i.e., acquiring knowledge in it, the path explored by an agent can be seen as a sequence of visited nodes. In this context, this thesis allowed us to observe the behavior of different network dynamics and topologies when acquiring knowledge. Moreover, using machine learning techniques, we have proposed a framework that showed it is possible to recover the generating structures that constructed an unknown sequence. Finally, we have evaluated how global properties of a network are reflected in structures generated by sequences. Thus, we have presented an analysis whether local information of a network are biased or it is indeed a real picture of the network as a whole; this analysis allowed us to measure the impact of the sequences size while identifying networks properties. Hence, the results presented in this thesis have shown the behavior of different structures during the knowledge acquisition process. Lastly, we can highlight the framework built in this work, which allowed to classify which are the original topology and dynamics that generated a given sequence. Such results may enable several applications in network science, and pave the knowledge in this area. Among the main results, this work has allowed the proper identification of sequences generating structures from the properties obtained during the reconstruction of such sequences as a complex network; and, moreover, it was possible to observe that small sequences allow the identification of the structures with high accuracyComplex networks have been used in several applications in the past few decades. Complex systems can be observed in applications such as transportation, energy systems, internet, biology, and logistics, among other possible *implementations*. In such structures, there may be agents exploring nodes and identifying new concepts and discovering the network; this kind of exploration is known as Knowledge Acquisition and it has been deeply researched for the past decades. When exploring a network, i.e., acquiring knowledge in it, the path explored by an agent can be seen as a sequence of visited nodes. In this context, this thesis allowed us to observe the behavior of different network dynamics and topologies when acquiring knowledge. Moreover, using machine learning techniques, we have proposed a framework that showed it is possible to recover the generating structures that constructed an unknown sequence. Finally, we have evaluated how global properties of a network are reflected in structures generated by sequences. Thus, we have presented an analysis whether local information of a network are biased or it is indeed a real picture of the network as a whole; this analysis allowed us to measure the impact of the sequences size while identifying networks properties. Hence, the results presented in this thesis have shown the behavior of different structures during the knowledge acquisition process. Lastly, we can highlight the framework built in this work, which allowed to classify which are the original topology and dynamics that generated a given sequence. Such results may enable several applications in network science, and pave the knowledge in this area. Among the main results, this work has allowed the proper identification of sequences generating structures from the properties obtained during the reconstruction of such sequences as a complex network; and, moreover, it was possible to observe that small sequences allow the identification of the structures with high accuracy

Redes complexas vêm sendo empregadas nas mais diversas aplicações há algumas décadas. Sistemas complexos podem ser vistos em aplicações como transportes, redes de energia, internet, biologia e logística, dentre outras possíveis implementações. Em tais estruturas é possível que existam agentes percorrendo os nós e identificando novos conceitos e descobrindo a rede; este tipo de exploração é conhecido como descoberta do conhecimento e vem sendo pesquisado profundamente nas últimas décadas. Quando explorando uma rede, ou seja, descobrindo conhecimento nela, o caminho percorrido pode ser visto como uma sequência de nós visitados. Esta tese foca no estudo da relação entre topologias, dinâmicas e sequências em redes complexas. Com isso, no desenvolvimento desta tese pudemos observar o comportamento de diferentes dinâmicas em diferentes topologias quando adquirindo conhecimento. Além disso, propusemos um framework que com o auxílio de técnicas de aprendizado de máquina demonstrou a possibilidade de se recuperar qual a estrutura geradora da sequência sem conhecê-la. Por fim, avaliamos como as propriedades globais de uma rede são refletidas em estruturas geradas por sequências, ou seja, apresentamos uma análise se informações locais estão enviesadas ou se, de fato, podem representar uma visão real da rede como um todo; esta análise permitiu ainda identificar o impacto do tamanho das sequências na identificação das propriedades da rede. Com isso, os resultados apresentados nesta tese demonstraram o comportamento de diferentes estruturas no processo de descoberta do conhecimento. Destacamos ainda, a construção de um framework para classificação da topologia da rede e dinâmica utilizadas na geração de sequências. Tais resultados permitem a viabilização de diversas aplicações em ciência das redes, além de fundamentar conhecimentos para a área. Dentre os principais resultados atingidos, este trabalho permitiu identificar estruturas geradoras de sequências a partir de propriedades obtidas durante a reconstrução destas sequências como uma rede complexa e, ainda, foi possível observar que sequências pequenas permitem a identificação das estruturas com alta acurácia.