Topological quantifiers were collected, and their final conclusions demonstrated that Iliad’s social network behaves most like a real social network. In a similar arguing, a pioneer study concerning three mythological narratives with uncertain historicity was made for Beowulf, Iliad and Táin Bó Cuailnge. This list of statistical properties of general networks enables us to classify most networks based on narratives as real or fictional. These networks have similar properties of that presented in and. To increase the examples of networks based on fictional networks, we list the networks studied by Carron and Kenna: Les Miserable, Richard III, The Lord of the Rings: The Fellowship of the Rings, and Harry Potter. These last references are related to Marvel Universe’s fictional network. Moreover, it holds giant component’s size larger than 90% of the total vertices, shows disassortativity by degree, and is both robust to random and targeted attacks. On the other hand, we have the fictional social networks, which can be characterized as being small world, feature hierarchical structure exponential law dependence of degree distribution, implying that it is not scale-free. They also possess giant component with less than 90% of the total number of vertices and are vulnerable to targeted attacks while robust to random attacks. Additionally, since real networks are scale-free, its degree distribution follows a power law. Real social networks are small world, hierarchically organized, highly clustered, assortatively mixed by degree, and scale-free. In this work, we are especially interested in two classes of social networks: real networks and fictional networks. This classification is made by means of topological measures that generate statistical properties which allow us to determine the class of networks. To address this problem, we rely on the concept of statistical universality to classify networks based on narrative into characteristic groups. As far as some narratives might contain uncertain historicity, so will be their resulting social networks. This procedure can be done if some standard criteria are met to avoid arbitrary tendencies. Ī priori, social networks can be extracted from any narrative that contains descriptions of social relationships. With conformity with this reasoning, we appeal to the statistical universality, using it to unify social network into characteristic groups. Beyond statistical properties, such social analysis also allows deep predictions, like human activity patterns over a spatial layout. In this manner, one can define a sort of taxonomy of social networks that can be built by the simple comparison of their statistical properties. The repertoire available of statistical measures of social networks increases as far as this procedure is applied to different cases. Introducing these concepts to the social sciences implies that we can build social networks from sets of observable sociological relations, such as individual interactions, associations of human groups, Internet’s social networks, etc. Given these structures, some patterns can be associated with networks measures which determine their classification. As the study of objects advances under the network’s paradigm, some classes of networks arise as a function of their statistical structures. Such areas include physics, social sciences, communication, economy, financial market, computer science, internet, World Wide Web, transportation, electric power distribution, molecular biology, ecology, neuroscience, linguistics, climate networks and so on. This process is accomplished as far as the fundamental concepts of complex network theory are applied to problems that may arise from many areas of study. The paradigm’s shift from reductionism to holism stands for a stepping stone that is taking researcher’s interests to the interdisciplinary approach.
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