Visual analysis of large graphs using (X,Y)-clustering and hybrid visualizations

Many different approaches have been proposed for the challenging problem of visually analyzing large networks. Clustering is one of the most promising. In this paper we propose a new goal for clustering that is especially tailored to hybrid-visualization tools. Namely, that of producing both intra-cluster graphs and inter-cluster graph that are suitable for highly-readable visualizations within different representation conventions. We formalize this concept in the (X,Y)-clustering framework, where Y is the class that defines the desired topological properties of intra-cluster graphs and X is the class that defines the desired topological properties of the inter-cluster graph. By exploiting this approach hybrid-visualization tools can effectively combine different node-link and matrix-based representations, allowing the users to interactively explore the graph by expansion/contraction of clusters without loosing their mental map. As a proof of concept, we describe the system VHYXY (Visual Hybrid (X,Y)-clustering) that integrates our techniques and we present the results of case studies to the visual analysis of co-authorship networks.