This article is addressing a recurrent problem in biology: mining newly built large scale networks. Our approach consists in comparing these new networks to well known ones. The visual backbone of this comparative analysis is provided by a network classification hierarchy. This method makes sense when dealing with metabolic networks since comparison could be done using pathways (clusters). Moreover each network models an organism and it exists organism classification such as taxonomies. Video demonstration: http://www.labri.fr/perso/bourqui/video.wmv 1 Background and motivation Visual mining of large networks is a challenging problem in biology since more and more large networks are inferred from high-throughput experiments (protein-protein interaction networks, metabolic networks [8] and gene networks [7]). The challenge is to understand the biological functions of their different parts. A way to circumvent this problem consists in fitting parts of the data onto available knowledg...