Visualizing Large and Clustered Networks

  • Katharina A. Lehmann
  • Stephan Kottler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4372)


The need to visualize large and complex networks has strongly increased in the last decade. Although networks with more than 1000 vertices seem to be prohibitive for a comprehensive layout, real-world networks exhibit a very inhomogenous edge density that can be harnessed to derive an aesthetic and structured layout. Here, we will present a heuristic that finds a spanning tree with a very low average spanner property for the non-tree edges, the so-called backbone of a network. This backbone can then be used to apply a modified tree-layout algorithm to draw the whole graph in a way that highlights dense parts of the graph, so-called clusters, and their inter-connections.


Span Tree Tree Path Cluster Network Optimization Heuristic Graph Drawing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Andersen, R., Chung, F., Lu, L.: Drawing power law graphs. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, Springer, Heidelberg (2005)Google Scholar
  2. 2.
    Baur, M., Brandes, U.: Crossing reduction in circular layouts. In: Hromkovič, J., Nagl, M., Westfechtel, B. (eds.) WG 2004. LNCS, vol. 3353, Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Carriére, J., Kazman, R.: Interacting with huge hierarchies: Beyond cone trees. In: Proceedings of the ACM conference on Information Visualization 1995, pp. 74–81. ACM Press, New York (1995)CrossRefGoogle Scholar
  4. 4.
    Derényi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Phys. Rev. Lett. 94, 160–202 (2005)CrossRefGoogle Scholar
  5. 5.
    Fekete, J.-D., Wang, D., Dang, N., Aris, A., Plaisant, C.: Overlaying graph links on treemaps. In: Proceedings of the IEEE Symposium on Information Visualization (InfoVis’03), IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  6. 6.
    Fruchtermann, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Software - Practice and Experience 21(11), 1129–1164 (1991)CrossRefGoogle Scholar
  7. 7.
    Garey, M.R., Johnson, D.S.: Computers and intractability. W.H. Freeman and Company, New York (1979)zbMATHGoogle Scholar
  8. 8.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 7821–7826 (2002)CrossRefMathSciNetzbMATHGoogle Scholar
  9. 9.
    Herman, I., Melançon, G., de Ruiter, M M., Delest, M.: Lecture Notes in Computer Science. In: North, S.C. (ed.) GD 1996. LNCS, vol. 1190, p. 392. Springer, Heidelberg (1997)Google Scholar
  10. 10.
    Lehmann, K.A., Kottler, S.: Visualizing Large and Clustered Networks. Technical Report of the Wilhelm-Schickard-Institut, WSI-2006-06, ISSN 0946-3852 (September 2006)Google Scholar
  11. 11.
    Newman, M., Barabasi, A.-L., Watts, D.J.: The structure and dynamics of networks. Princeton University Press, Princeton (2006)zbMATHGoogle Scholar
  12. 12.
    Noack, A.: An energy model for visual graph clustering. In: Liotta, G. (ed.) GD 2003. LNCS, vol. 2912, Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Noack, A.: Energy-based clustering of graphs with nonuniform degrees. In: Healy, P., Nikolov, N.S. (eds.) GD 2005. LNCS, vol. 3843, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814 (2005)CrossRefGoogle Scholar
  15. 15.
    Mueller, S.: OrganicLayouter in the yFiles library, Version 2.2.

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Katharina A. Lehmann
    • 1
  • Stephan Kottler
    • 1
  1. 1.University of Tübingen, Wilhelm-Schickard-Institute, Sand 14, 72076 TübingenGermany

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