Multiscale Visualization of Small World Networks

David Auber (1) , Yves Chiricota (2) , Fabien Jourdan (3) , Guy Melançon (3)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 UQAC - Université du Québec à Chicoutimi
3 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier

Many networks under study in Information Visualization are "small world" networks. These networks first appeared in the study social networks and were shown to be relevant models in other application domains such as software reverse engineering and biology. Furthermore, many of these networks actually have a multiscale nature: they can be viewed as a network of groups that are themselves small world networks. We describe a metric that has been designed in order to identify the weakest edges in a small world network leading to an easy and low cost filtering procedure that breaks up a graph into smaller and highly connected components. We show how this metric can be exploited through an interactive navigation of the network based on semantic zooming. Once the network is decomposed into a hierarchy of sub-networks, a user can easily find groups and subgroups of actors and understand their dynamics.

Keywords: Multiscale graphs; Clustering metric; Semantic zooming

Domains: Computer Science [cs]

David Auber, Yves Chiricota, Fabien Jourdan, Guy Melançon. Multiscale Visualization of Small World Networks. InfoVis: Information Visualization, Oct 2003, Seattle, WA, United States. pp.75-81. ⟨lirmm-00269770⟩