  Text network analysis has received increasing attention as a consequence of
its wide range of applications. In this work, we extend a previous work founded
on the study of topological features of mesoscopic networks. Here, the
geometrical properties of visualized networks are quantified in terms of
several image analysis techniques and used as subsidies for authorship
attribution. It was found that the visual features account for performance
similar to that achieved by using topological measurements. In addition, the
combination of these two types of features improved the performance.
