Contextual Visualization of Actor Status in Social Networks

  • Ulrik Brandes
  • Dorothea Wagner
Conference paper
Part of the Eurographics book series (EUROGRAPH)


We propose a novel information visualization approach for an analytical method applied in the social sciences. In social network analysis, social structures are formally represented as graphs, and structural properties of these graphs are assumed to be useful in the explanation of social phenomena. A particularly important such property is the relative status of actors in a given network.

Since operationalizations of status are aggregate indices of vertices, researchers are not only interested in status, but also in the context leading to these values, i.e. the underlying social network. We therefore visualize the network in a layered fashion, mapping status scores to vertical coordinates. The resulting problem of determining horizontal positions of vertices such that the over all layout is readable, is algorithmically difficult, yet well-studied in the literature on graph drawing. We outline a customized approach that routinely produces satisfactory pictures at interactive speed.


Social Network Analysis Project Home Page Aggregate Index Bend Point 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen. LAPACK User’s Guide. Society for Industrial and Applied Mathematics, 3rd edition, 1999. See Scholar
  2. 2.
    V. Batagelj and A. Mrvar. Pajek - Program for large network analysis. Connections, 21(2)47–57, 1998. Project home page at Scholar
  3. 3.
    R.A. Becker, S.G. Eick, and A.R. Wilks. Visualizing network data. IEEE Transactions on Visualization and Graphics, 1(1)16–28, 1995.CrossRefGoogle Scholar
  4. 4.
    J. Bertin. Semiology of Graphics: Diagrams, Networks, Maps. University of Wisconsin Press, 1983.Google Scholar
  5. 5.
    . U. Brandes, P. Kenis, J. Raab, V. Schneider, and D. Wagner. Explorations into the visualization of policy networks. Journal of Theoretical Politics, 11(1)75–106 1999.CrossRefGoogle Scholar
  6. 6.
    U. Brandes, P. Kenis, and D. Wagner. Centrality in policy network drawings. In Kratochvil [19], pages 250–258.Google Scholar
  7. 7.
    R.S. Burt. Structure, Version 4.2. Center for the Social Sciences, Columbia University, New York, 1991.Google Scholar
  8. 8.
    G. Di Battista, P. Eades, R. Tamassia, and I.G. Tollis. Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall, 1999.Google Scholar
  9. 9.
    P. Eades. A heuristic for graph drawing. Congressus Numerantium, 42:149–160, 1984.MathSciNetGoogle Scholar
  10. 10.
    P. Eades and N.C. Wormald. Edge crossings in drawings of bipartite graphs. Algorithmica, 11:379–403, 1994.MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    E.R. Gansner, S.C. North, andK.-P. Vo. DAG - A program that draws directed graphs. Software-Practice and Experience, 17(1)1047–1062, 1988.CrossRefGoogle Scholar
  12. 12.
    M.R. Garey and D.S. Johnson. Crossing number is NP-complete. SIAM Journal on Algebraic and Discrete Methods, 4:312–316, 1983MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    N.B. Harrison and J.O. Coplien. Patterns of productive Software organziations. Bell Labs Technical Journal, pages 138–145 Summer 1996.Google Scholar
  14. 14.
    M. Jünger and P. Mutzel. 2-Layer straightline crossing minimization: Performance of exact and heuristic algorithms. Journal on Graph Algorithms and Applications, 1(1)1–25 1997.Google Scholar
  15. 15.
    R.M. Karp. Reducibility among combinatorial problems. In R. Miller and J. Thatcher, editors, Complexity of Computer Computations, pages 85-103. Plenum Press, 1972.Google Scholar
  16. 16.
    L. Katz. A new status index derived from sociometric analysis. Psychometrika, 18:39–43 1953.MATHCrossRefGoogle Scholar
  17. 17.
    D. Krackhardt. Social networks and the liability of newness for managers. In C.L. Cooper and D.M. Rousseau, editors, Trends in Organizational Behavior, volume 3, pages 159-173. John Wiley & Sons, 1996.Google Scholar
  18. 18.
    D. Krackhardt, J. Blythe, and C. McGrath. KrackPlot 3.0: An improved network drawing program. Connections, 17(2):53–55, 1994. Project home page at Scholar
  19. 19.
    J. Kratochvíl, editor. Proceedings of the 7th International Symposium on Graph Drawing (GD ′99), volume 1731 of Lecture Notes in Computer Science. Springer, 1999.Google Scholar
  20. 20.
    C. Matuszewski, R. Schönfeld, and P. Molitor. Using sifting for k-layer straightline crossing minimization. In Kratochvíl [19], pages 217-224.Google Scholar
  21. 21.
    C. McGrath, J. Blythe, and D. Krackhardt. The effect of spatial arrangement on judgments and errors in interpreting graphs. Social Networks, 19(3)223–242, 1997.CrossRefGoogle Scholar
  22. 22.
    K. Mehlhorn and S. Näher. The LEDA Platform of Combinatorial and Geometrie Computing. Cambridge University Press, 1999. Project home page at Scholar
  23. 23.
    P. Mutzel, C. Gutwenger, R. Brockenauer, S. Fialko, G.W. Klau, M. Krüger, T. Ziegler, S. Näher, D. Alberts, D. Ambras, G. Koch, M. Jünger, C. Buchheim, and S. Leipert. A library of algorithms for graph drawing. In S.H. Whitesides, editor, Proceedings of the 6th International Symposium on Graph Drawing (GD ’98), volume 1547 of Lecture Notes in Computer Science, pages 456-457. Springer, 1998. Project home page at Scholar
  24. 24.
    H.C. Purchase. Which aesthetic has the greatest effect on human understanding? In G. Di Battista, editor, Proceedings of the 5th International Symposium on Graph Drawing (GD ’97), volume 1353 of Lecture Notes in Computer Science, pages 248- 261. Springer, 1997.Google Scholar
  25. 25.
    H.C. Purchase, R.F. Cohen, and M. James. An experimental study of the basis for graph drawing algorithms. ACM Journal of Experimental Algorithmics, 2(4), 1997.Google Scholar
  26. 26.
    J. Raab. Steuerungsstrukturen politisierter Großprivatisierungen in Ostdeutschland 1990-1994. Das Beispiel der Werft- und Stahlindustrie. PhD thesis, University of Konstanz, 2000.Google Scholar
  27. 27.
    W.D. Richards. MultiNet. Software tool, see Multinet/.Google Scholar
  28. 28.
    K. Sugiyama, S. Tagawa, and M. Toda. Methods for visual understanding of hierarchical system struetures. IEEE Transactions on Systems, Man and Cybernetics, 11(2): 109–125, February 1981.MathSciNetCrossRefGoogle Scholar
  29. 29.
    S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ulrik Brandes
    • 1
  • Dorothea Wagner
    • 1
  1. 1.Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany

Personalised recommendations