Visual Analysis Based on Dominator Trees with Application to Personalized eLearning

Luigi Laura, Umberto Nanni, Marco Temperini

Abstract: The visualization of large graphs in interactive applications, specifically on small devices, can make harder to understand and analyze the displayed information. We show as simple topological properties of the graph can provide an efficient automatic computation of properties which improves the “readability” of a large graph by a proper selection of the displayed information. We show an approach to the visualization of a learning activity based on connectivity and related concepts as effective tools for visual analysis by learners, and by administrator of a repository.