One of the most interesting scientific challenges nowadays deals with the analysis and the understanding of complex networks' dynamics and how their processes lead to emergence according to the interactions among their components. In this paper we approach the definition of new methodologies for the visualization and the exploration of the dynamics at play in real dynamic social networks.We present a recently introduced formalism called TVG (for time-varying graphs), which was initially developed to model and analyze highly-dynamic and infrastructure-less communication networks. As an application context, we chose the case of scientific communities by analyzing a portion of the ArXiv repository (ten years of publications in physics). The analysis presented in the paper passes through different data transformations aimed at providing different perspectives on the scientific community and its evolutions. On a first level we discuss the dataset by means of both a static and temporal analysis of citations and co-authorships networks. Afterward, as we consider that scientific communities are at the same time communities of practice (through coauthorship) and that a citation represents a deliberative selection pointing out the relevance of a work in its scientific domain, we introduce a new transformation aimed at capturing the interdependencies between collaborations' patterns and citations' effects and how they make evolve a goal oriented systems as Science. Finally, we show how through the TVG formalism and derived indicators, it is possible to capture the interactions patterns behind the emergence (selection) of a sub-community among others, as a goal-driven preferential attachment toward a set of authors among which there are some key scientists (Nobel prizes) acting as attractors on the community.