## 2 Related Works
In [20], RoG is used on geo-localized data from Twitter to study urban systems and human dynamics to help urban planning and policy making. However, these tools can be an appropriate choice also when a geographic or spatial reference is missing in the given domain. In this case, the definition of a geometric space depends on the task and on the available data. Recently, several works leverage word embedding spaces to get insights from text. Word embedding algorithms provide a framework where semantic distances among terms can be evaluated. Thesecomputationally efficient predictive models turn words or phrases into vectors of real numbers in a multi-dimensional space, where semantically related words are mapped to nearby points [21; 22]. Among those algorithms, Word2 Vec [23] has reached the status of a consolidated tool. Word embedding approach has been already considered in the trajectory domain mostly for trajectory representation learning. In [24], the authors demonstrate that a representation for spatial trajectories can be learned using Word2 vec by clustering GPS coordinates and exploiting this form to see trajectories as word sentences. These embeddings are then used to predict users' demographic information. A very similar approach is presented also in [25] where the goal is to learn a representation from people's trajectories with a neural network to identify and predict social lifestyles. However, all of these approaches consider always trajectories in the geographic realm. In [26], word embedding is used for topic modeling to analyze and explain the behavior of the CiteScore metrics for journals indexed in Scopus in 2017 and to get statistics about the impact of topics. In [27], the authors demonstrated how the semantic space built with Word2 Vec on large corpora, combined with network science tools (such as degrees, distances and clustering coefficient) can be relevant for knowledge discovery. All of this progress in knowledge extraction, human mobility and in Natural Language Processing, motivates our choice of investigating neural network embedding spaces with Radius of Gyration, to exploit the semantic space and its vector properties. As a separate research area, several works are conducted in the field of knowledge discovery from bibliographic databases [28; 29]. Scopus, together with Web of Science, represents one of the most authoritative bibliographic databases [30]. In addition to the abstracts of articles, it provides access to the references included in those articles, allowing users to search both forward and backward in time [31]. In [32], data mining techniques are applied to gather and analyze data from the Scopus repository. A social graph of scientific authors is created, starting from citations among the articles. Moreover, using data mining techniques, some relevant research topics are inferred for each author, from the textual analysis of the abstracts of their articles. The study shows the emergence of some clusters of topics, which are studied by distinct groups of authors. In [33], the relation between interdisciplinarity and citation impact of individual publications is analyzed for four different scientific fields. Thus, the study does not analyze researchers' mobility across topics, but the impact of multidisciplinary and cross-topic research. The article is relevant because it finds results that are somewhat similar to those highlighted in our own study. In general, interdisciplinarity is seen as a highly positive criterion for obtaining the most prestigious grants. However, the authors observe that, although the combination of multiple fields has a positive effect in creating new knowledge, successful research is best achieved by focusing on related fields of knowledge. Instead, interdisciplinary research in fields that are very different could have a higher chance to fail.