PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Visual analysis of large graphs using (X,Y)-clustering and hybrid visualizations
Vladimir Batagelj, Franz J. Brandenburg, Walter Didimo, Giuseppe Liotta, Pietro Palladino and Maurizio Patrignani
IEEE trans. vis. comput. graph Volume 17, Number 11, pp. 1587-1598, 2011. ISSN 1077-2626

Abstract

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 clustering technique whose goal is that of producing both intracluster graphs and intercluster graph with desired topological properties. We formalize this concept in the (X,Y) -clustering framework, where Y is the class that defines the desired topological properties of intracluster graphs and X is the class that defines the desired topological properties of the intercluster graph. By exploiting this approach, hybrid visualization tools can effectively combine different node-link and matrix-based representations, allowing 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 Visual Hybrid (X,Y)-clustering (VHYXY) that implements our approach and we present the results of case studies to the visual analysis of social networks.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:9249
Deposited By:Boris Horvat
Deposited On:21 February 2012