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Visual analysis of graphs with multiple connected components

: Landesberger, Tatiana von; Görner, Melanie; Schreck, Tobias

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Symposium on Visual Analytics Science and Technology, VAST 2009 : Co-located with the annual IEEE Visualization Conference (IEEE Vis) and the IEEE Information Visualization Conference (IEEE InfoVis), 11 - 16 October 2009, Atlantic City, New Jersey, USA
Piscataway/NJ: IEEE, 2009
ISBN: 1-4244-5284-8
ISBN: 978-1-4244-5284-2
ISBN: 978-1-4244-5283-5
Symposium on Visual Analytics Science and Technology (VAST) <2009, Atlantic City/NJ>
Fraunhofer IGD ()
clustering; Graphical User Interface (GUI); image generation; graph; self-organizing map

In this paper, we present a system for the interactive visualization and exploration of graphs with many weakly connected components. The visualization of large graphs has recently received much research attention. However, specific systems for visual analysis of graph data sets consisting of many such components are rare. In our approach, we rely on graph clustering using an extensive set of topology descriptors. Specifically, we use the Self-Organizing- Map algorithm in conjunction with a user-adaptable combination of graph features for clustering of graphs. It offers insight into the overall structure of the data set. The clustering output is presented in a grid containing clusters of the connected components of the input graph. Interactive feature selection and task-tailored data views allow the exploration of the whole graph space. The system provides also tools for assessment and display of cluster quality. We demonstrate the usefulness of our system by application to a shareholder structure analysis problem based on a large real-world data set. While so far our approach is applied to weighted directed graphs only, it can be used for various graph types.