Using phylogenetic trees to generate semantic meaningful edge bundles

Graphs have been successfully applied in a range of problems and applications. It is the object of study of different areas, from modeling and analysis to the construction of visual representations. Different approaches exist for graph visualization, however, most of them suffer from the severe clutter when the number of nodes or edges is large. Amongst the techniques of graph visualization that handle such problem, Edge-bundling techniques attained relative success on improving the quality of the visual representations by bending and aggregating edges in order to reduce overlapping. Despite this success, most of them just perform the bundle based only on the visual space information, that is, there is no explicit connection between the produced bundled visual representation and the data. Some of them look upon edge information as a complement to the visual information, but it is just an additional measure, not their main goal. In this paper, we present a novel edge-bundling technique, called Neighbor Joining Bundling (NJB), for graph visualization that tackles this problem by considering the similarity between the nodes when performing the edges bending.FAPESP (#2014/18665-1 and #2011/22749-8)CapesCNP