MatLink: Enhanced Matrix Visualization for Analyzing Social Networks

  • Nathalie Henry
  • Jean-Daniel Fekete
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4663)

Abstract

Visualizing social networks presents challeges for both node-link and adjacency matrix representations. Social networks are locally dense, which makes node-link displays unreadable. Yet, main analysis tasks require following paths, which is difficult on matrices. This article presents MatLink, a hybrid representation with links overlaid on the borders of a matrix and dynamic topological feedback as the pointer moves. We evaluated MatLink by an experiment comparing its readability, in term of errors and time, for social network-related tasks to the other conventional representations on graphs varying in size (small and medium) and density. It showed significant advantages for most tasks, especially path-related ones where standard matrices are weak.

Keywords

Node-Link Diagram Matrix Visualization Social Network 

References

  1. 1.
    Ghoniem, M., Fekete, J.D., Castagliola, P.: On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Information Visualization 4(2), 114–135 (2005)CrossRefGoogle Scholar
  2. 2.
    Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)CrossRefMATHGoogle Scholar
  3. 3.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  4. 4.
    Amar, R., Eagan, J., Stasko, J.: Low-level components of analytic activity in information visualization. In: Proceedings of the IEEE Symposium on Information Visualization, pp. 111–117. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  5. 5.
    Plaisant, C., Lee, B., Parr, C.S., Fekete, J.D., Henry, N.: Task taxonomy for graph visualization. In: BEyond time and errors: novel evaLuation methods for Information Visualization (BELIV’06), Venice, Italy, pp. 82–86. ACM Press, New York (2006)Google Scholar
  6. 6.
    Heer, J., Boyd, D.: Vizster: Visualizing Online Social Networks. In: Proceedings of the IEEE Symposium on Information Visualization vol. 5 (2005)Google Scholar
  7. 7.
    de Nooy, W., Mrvar, A., Batagelj, V.: Exploratory Social Network Analysis with Pajek. In: Structural Analysis in the Social Sciences, Cambridge University Press, Cambridge (2005)Google Scholar
  8. 8.
    Adar, E.: Guess: a language and interface for graph exploration. In: CHI 2006. Proceedings of the SIGCHI conference on Human Factors in computing systems, New York, NY, USA, pp. 791–800. ACM Press, New York (2006)Google Scholar
  9. 9.
    Doreian, P., Batagelj, V., Ferligoj, A.: Generalized Blockmodeling. In: Structural Analysis in the Social Sciences, Cambridge University Press, Cambridge (2005)Google Scholar
  10. 10.
    Henry, N., Fekete, J.D.: MatrixExplorer: a Dual-Representation System to Explore Social Networks. IEEE Transactions on Visualization and Computer Graphics 12(5), 677–684 (2006)CrossRefGoogle Scholar
  11. 11.
    Di Battista, G., Eades, P., Tamassia, R., Tollis, I.G: Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall, Englewood Cliffs (1998)MATHGoogle Scholar
  12. 12.
    Noack, A.: Energy-based clustering of graphs with nonuniform degrees. In: Healy, P., Nikolov, N.S. (eds.) GD 2005. LNCS, vol. 3843, pp. 309–320. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Auber, D., Chiricota, Y., Jourdan, F., Melancon, G.: Multiscale visualization of small world networks. In: Proceedings of the IEEE Symposium on Information Visualization, pp. 75–81. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  14. 14.
    Wattenberg, M.: Visual exploration of multivariate graphs. In: Proceedings of the SIGCHI conference on Human Factors in computing systems, Montréal, Québec, Canada, pp. 811–819. ACM Press, New York (2006)CrossRefGoogle Scholar
  15. 15.
    Bertin, J.: Semiology of graphics. University of Wisconsin Press (1983)Google Scholar
  16. 16.
    Abello, J., van Ham, F.: Matrix zoom: A visual interface to semi-external graphs. In: Proceedings of the IEEE Symposium on Information Visualization (INFOVIS’04), Austin, Texas, pp. 183–190. IEEE Computer Society Press, Los Alamitos (2004)CrossRefGoogle Scholar
  17. 17.
    van Ham, F.: Using multilevel call matrices in large software projects. In: Proceedings of the IEEE Symposium on Information Visualization, Seattle, WA, USA, pp. 227–232. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  18. 18.
    Fekete, J.D.: The InfoVis Toolkit. In: Proceedings of the IEEE Symposium on Information Visualization, pp. 167–174. IEEE Computer Society Press, Los Alamitos (2004)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Nathalie Henry
    • 1
    • 2
  • Jean-Daniel Fekete
    • 1
  1. 1.INRIA/LRI, Bat. 490, Univ. Paris-Sud, F91405, OrsayFrance
  2. 2.Univ. of SydneyAustralia

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