  Authorship attribution is a natural language processing task that has been
widely studied, often by considering small order statistics. In this paper, we
explore a complex network approach to assign the authorship of texts based on
their mesoscopic representation, in an attempt to capture the flow of the
narrative. Indeed, as reported in this work, such an approach allowed the
identification of the dominant narrative structure of the studied authors. This
has been achieved due to the ability of the mesoscopic approach to take into
account relationships between different, not necessarily adjacent, parts of the
text, which is able to capture the story flow. The potential of the proposed
approach has been illustrated through principal component analysis, a
comparison with the chance baseline method, and network visualization. Such
visualizations reveal individual characteristics of the authors, which can be
understood as a kind of calligraphy.
