There any many different aspects in natural languages and their related dynamics that have been studied. In the case of languages, some quantitative analyses have been done by using stochastic models. Furthermore, natural languages can be understood as complex systems. Thus, there is a possibility to use set of tools development to analyse complex networks, which are computationally represented by graphs, also to analyse natural languages. Furthermore, these tools can be used to represent and analyse some related dynamics taking place on the networks. Observe that knowledge is intrinsically related to language, because language is the vehicle used by humans beings to transmit dicoveries, and the language itself is also a type of knowledge. This thesis is divided into two types of analyses: (i) texts and (II) dynamical aspects. In the first part, we proposed networks representations of text in different scales analyses, starting from the analysis of writing style considering word adjacency networks (co-occurence) to understand local patterns of words, to a mesoscopic representation, which is created from chunks of text and grasps information of the unfolding of the story. In the second part, we considered the structure and dynamics related to knowledge and language, in this case, starting from the larger scale, in which we studied the connectivity between applied and theoretical physics. In the following, we simulated the knowledge acquisition by researchers in a multi-agent dynamics and an intelligent machine that solves problems, which is represented by a network. At the smallest considered scale, we simulate the transmission of networks. This transmission considers the data as a series of organized symbols that is obtained from a dynamics. In order to improve the speed of transmission, the series can be compacted. For that, we considered the information theory and Huffman code. The proposed network-based approaches were found to be suitable to deal with the employed analysis for all of the tested scales.