Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

We present a strategy for analyzing large, social small-world graphs, such as those formed by human networks. Our approach brings together ideas from a number of different research areas, including graph layout, graph clustering and partitioning, machine learning, and user interface design. It helps users explore the networks and develop insights concerning their members and structure that may be difficult or impossible to discover via traditional means, including existing graph visualization and/or statistical methods.

REFERENCES

• 1: Christopher Ahlberg , Ben Shneiderman, Visual information seeking: tight coupling of dynamic query filters with starfield displays, Proceedings of the SIGCHI conference on Human factors in computing systems: celebrating interdependence, p.313-317, April 24-28, 1994, Boston, Massachusetts, United States [doi>10.1145/191666.191775]
• 2: Albert, R., and Baraba si, A.-L. Statistical mechanics of complex networks. Rev. Mod. Phys. 74 (2002), 47--97.
• 3: Albert, R., Jeong, H., and Bar'a basi, A.-L. Diameter of the world wide web. Nature 401 (1999), 130--131.
• 4: Auber, D., Chiricota, Y., Jourdan, F., and Melancon, G. Multiscale visualization of small world networks. In Proceedings of the 2003 IEEE Symposium on Information Visualization (2003), pp. 75--81.
• 5: Thomas M. J. Fruchterman , Edward M. Reingold, Graph drawing by force-directed placement, Software—Practice & Experience, v.21 n.11, p.1129-1164, Nov. 1991 [doi>10.1002/spe.4380211102]
• 6: Ivan Herman , M. Scott Marshall , Guy Melançon, Density Functions for Visual Attributes and Effective Partitioning in Graph Visualization, Proceedings of the IEEE Symposium on Information Vizualization 2000, p.49, October 09-10, 2000
• 7: Herman, I., Marshall, M. S., Melancon, G., Duke, D. J., Delest, M., and Domenger, J.-P. Skeletal images as visual cues in graph visualization. In Data Visualization '99, E. Gröller, H. Löffelmann, and W. Ribarsky, Eds. Springer-Verlag Wien, 1999, pp. 13--22.
• 8: Adriana Ioana Iamnitchi , Ian Foster, Resource discovery in large resource-sharing environments, 2003
• 9: T. Kohonen, Self-organization and associative memory: 3rd edition, Springer-Verlag New York, Inc., New York, NY, 1989
• 10: Bernd Meyer, Self-Organizing Graphs - A Neural Network Perspective of Graph Layout, Proceedings of the 6th International Symposium on Graph Drawing, p.246-262, August 01, 1998
• 11: Tamara Munzner, Exploring Large Graphs in 3D Hyperbolic Space, IEEE Computer Graphics and Applications, v.18 n.4, p.18-23, July 1998 [doi>10.1109/38.689657]
• 12: Noack, A. An energy model for visual graph clustering. In Proceedings of the 11th International Symposium on Graph Drawing (2003), pp. 425--436.
• 13: Janet M. Six , Ioannis G. Tollis, Effective Graph Visualization Via Node Grouping, Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01), p.51, October 22-23, 2001
• 14: Stasko, J. T., and Wehrli, J. F. Three-dimensional computation visualization. In Proc. IEEE Symp. Visual Languages, VL (24-27 1993), E. P. Glinert and K. A. Olsen, Eds., IEEE Computer Society, pp. 100--107.
• 15: van Dongen, S. Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht, 2000.
• 16: Frank van Ham , Jarke J. van Wijk, Interactive Visualization of Small World Graphs, Proceedings of the IEEE Symposium on Information Visualization (INFOVIS'04), p.199-206, October 10-12, 2004 [doi>10.1109/INFOVIS.2004.43]
• 17: Colin Ware , Glenn Franck, Evaluating stereo and motion cues for visualizing information nets in three dimensions, ACM Transactions on Graphics (TOG), v.15 n.2, p.121-140, April 1996 [doi>10.1145/234972.234975]
• 18: Watts, D., and Strogatz, S. Collective dynamics of small-world networks. Nature 393 (1998), 440--442.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.6 Methodology and Techniques
          Subjects: Interaction techniques

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

Keywords:
graph clustering, graph layout, histogram, information visualization, machine learning, self-organizing map, small-world graph, social networks, user interface design