A Potential Field Function for Overlapping Point Set and Graph Cluster Visualization
In this paper we address the problem of visualizing overlapping sets of points with a fixed positioning in a comprehensible way. A standard visualization technique is to enclose the point sets in isocontours generated by bounding a potential field function. The most commonly used functions are various approximations of the Gaussian distribution. Such an approach produces smooth and appealing shapes, however it may produce an incorrect point nesting in generated regions, e.g. some point is contained inside a foreign set region. We introduce a different potential field function that keeps the desired properties of Gaussian distribution, and in addition guarantees that every point belongs to all its sets’ regions and no others, and that regions of two sets with no common points have no overlaps.
The presented function works well if the sets intersect each other, a situation that often arises in social network graphs, producing regions that reveal the structure of their clustering. It performs best when the graphs are positioned by force-directed layout algorithms. The function can also be used to depict hierarchical clustering of the graphs. We study the performance of the method on various real-world graph examples.
KeywordsInformation visualization Implicit surfaces
- 1.Balzer, M., Deussen, O.: Level-of-detail visualization of clustered graph layouts. In: 2007 6th International AsiaPacific Symposium on Visualization, pp. 133–140 (2007)Google Scholar
- 8.Gansner, E.R., Hu, Y., Kobourov, S.: GMap: Visualizing graphs and clusters as maps. In: 2010 IEEE Pacific Visualization Symposium PacificVis, pp. 201–208 (2010)Google Scholar
- 10.Gross, M.H., Sprenger, T.C., Finger, J.: Visualizing information on a sphere. In: Proceedings of the 1997 Conference on Information Visualization, pp. 11–16 (1997)Google Scholar
- 11.Heer, J., Boyd, D.: Vizster: visualizing online social networks. In: IEEE Symposium on Information Visualization 2005 INFOVIS 2005, vol. 5, pp. 32–39 (2003)Google Scholar
- 13.Krebs, V.: Managing the 21st century organization. IHRIM J. XI(4), 2–8 (2007)Google Scholar
- 14.Matsumoto, Y., Umano, M., Inuiguchi, M.: Visualization with Voronoi tessellation and moving output units in self-organizing map of the real-number system. Neural Netw. 1, 3428–3434 (2008)Google Scholar
- 19.Sprenger, T.C., Brunella, R., Gross, M.H.: H-BLOB: a hierarchical visual clustering method using implicit surfaces. In: Visualization 2000. Proceedings, pp. 61–68, October 2000Google Scholar
- 20.Van Ham, F., Van Wijk, J.J.: Interactive visualization of small world graphs. In: IEEE Symposium on Information Visualization, pp. 199–206 (2004)Google Scholar
- 21.Watanabe, N., Washida, M., Igarashi, T.: Bubble clusters: an interface for manipulating spatial aggregation of graphical objects. In: Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, UIST 2007, pp. 173–182. ACM, New York (2007)Google Scholar