VINCENT - Visualization of Network Centralities

Andreas Kerren, Harald Köstinger, Björn Zimmer

2012

Abstract

The use of network centralities in the field of network analysis plays an important role when the relative importance of nodes within the network topology should be rated. A single network can easily be represented by the use of standard graph drawing algorithms, but not only the exploration of one centrality might be important: the comparison of two or more of them is often crucial for a better understanding. When visualizing the comparison of several network centralities, we are facing new problems of how to show them in a meaningful way. For instance, we want to be able to track all the changes of centralities in the networks as well as to display the single networks as best as possible. In the life sciences, centrality measures help scientists to understand the underlying biological processes and have been successfully applied to different biological networks. The aim of this paper is to present a novel system for the interactive visualization of biochemical networks and its centralities. Researchers can focus on the exploration of the centrality values including the network structure without dealing with visual clutter or occlusions of nodes. Simultaneously, filtering based on statistical data concerning the network elements and centrality values supports this.

References

  1. Albrecht, M., Kerren, A., Klein, K., Kohlbacher, O., Mutzel, P., Paul, W., Schreiber, F., and Wybrow, M. (2010). On open problems in biological network visualization. In Proc. International Symposium on Graph Drawing (GD 7809), volume 5849 of LNCS, pages 256-267. Springer.
  2. Brewer, C. A. (last accessed: 2011-03-22). ColorBrewer. http://colorbrewer2.org/, 2nd edition.
  3. Correa, C. D., Crnovrsanin, T., Muelder, C., Shen, Z., Armstrong, R., Shearer, J., and Ma, K.-L. (2008). Cell phone mini challenge award: Intuitive social network graphs visual analytics of cell phone data using mobivis and ontovis. In Visual Analytics Science and Technology, 2008. VAST 7808. IEEE Symposium on, pages 211 -212.
  4. Correa, C. D. and Ma, K.-L. (2011). Visualizing social networks. In Aggarwal, C., editor, Social Network Data Analytics, pages 307-326. Springer.
  5. Di Battista, G., Eades, P., Tamassia, R., and Tollis, I. G. (1999). Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall.
  6. Dwyer, T., Hong, S.-H., Koschützki, D., Schreiber, F., and Xu, K. (2006). Visual analysis of network centralities. In Misue, K., Sugiyama, K., and Tanaka, J., editors, Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation (APVis'06), pages 189-198, Darlinghurst, Australia. Australian Computer Society, ACM International Conference Proceeding Series, vol. 164.
  7. Görg, C., Pohl, M., Qeli, E., and Xu, K. (2007). Visual Representations. In Kerren, A., Ebert, A., and Meyer, J., editors, Human-Centered Visualization Environments, LNCS Tutorial 4417, pages 163-230. Springer.
  8. Heer, J., Card, S. K., and Landay, J. A. (2005). Prefuse: a toolkit for interactive information visualization. In Proceedings of the SIGCHI conference on Human factors in computing systems, CHI 7805, pages 421-430, New York, NY, USA. ACM.
  9. Henry, N., Fekete, J.-D., and Mcguffin, M. J. (2007). Nodetrix: a hybrid visualization of social networks. IEEE Transactions on Visualization and Computer Graphics (IEEE Visualization Conference and IEEE Conference on Information Visualization) Proceedings, 13:1302-1309.
  10. Holten, D. (2006). Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data. IEEE Transactions on Visualization and Computer Graphics, 12(5).
  11. Holten, D. and van Wijk, J. J. (2009). Force-directed edge bundling for graph visualization. IEEE-VGTC Symposium on Visualization 2009, 28(3).
  12. Jia, Y., Hoberock, J., Garland, M., and John C. Hart, Member, I.-C. (2008). On the visualization of social and other scale-free networks. IEEE Transactions on Visualization and Computer Graphics, 14(6):1285-1292.
  13. Junker, B., Koschutzki, D., and Schreiber, F. (2006). Exploration of biological network centralities with centibin. BMC Bioinformatics, 7(1):219.
  14. Junker, B. H. and Schreiber, F. (2008). Analysis of Biological Networks. Wiley Series on Bioinformatics, Computational Techniques and Engineering. Wiley.
  15. Jusufi, I., Dingjie, Y., and Kerren, A. (2010). The network lens: Interactive exploration of multivariate networks using visual filtering. In Information Visualisation (IV), 2010 14th International Conference, pages 35 - 42.
  16. Jusufi, I., Klukas, C., Kerren, A., and Schreiber, F. (2011). Guiding the interactive exploration of metabolic pathway interconnections. Information Visualization. (to appear).
  17. Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transaction on Visualization and Computer Graphics, 8(1):1-8.
  18. Kerren, A. and Köstinger, H. (2011). Interactive exploration and analysis of network centralities. Interactive Poster, EuroVis 11, Bergen, Norway.
  19. Koschützki, D. and Schreiber, F. (2004). Comparison of centralities for biological networks. In R. Giegerich, J. S., editor, Proc. German Conf. Bioinformatics (GCB04), pages 199-206.
  20. Köstinger, H. (2011). Vincent - visualization of network centralities. Master's thesis, Linnaeus University, School of Computer Science, Physics and Mathematics, Växjö, Sweden.
  21. Newman, M. E. J. (2003). A measure of betweenness centrality based on random walks. arXiv condmat/0309045.
  22. Newman, M. E. J. (2010). Networks: An Introduction. Oxford University Press.
  23. Roberts, J. C. (2007). State of the art: Coordinated & multiple views in exploratory visualization. In Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization, pages 61-71, Washington, DC, USA. IEEE Computer Society.
  24. Salwinski, L., Miller, C. S., Smith, A. J., Pettit, F. K., Bowie, J. U., and Eisenberg, D. (2004). The database of interacting proteins: 2004 update. Nucleic Acids Research, 32(1):449-451.
  25. Scardoni, G., Petterlini, M., and Laudanna, C. (2009). Analyzing biological network parameters with centiscape. Bioinformatics, 25(21):2857-2859.
  26. Shneiderman, B. (1994). Dynamic queries for visual information seeking. IEEE Software, 11:70-77.
  27. Tweedie, L., Spence, B., Williams, D., and Bhogal, R. (1994). The attribute explorer. CHI'94 - Celebrating Interdependence, pages 435-436.
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Paper Citation


in Harvard Style

Kerren A., Köstinger H. and Zimmer B. (2012). VINCENT - Visualization of Network Centralities . In Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2012) ISBN 978-989-8565-02-0, pages 703-712. DOI: 10.5220/0003822207030712


in Bibtex Style

@conference{ivapp12,
author={Andreas Kerren and Harald Köstinger and Björn Zimmer},
title={VINCENT - Visualization of Network Centralities},
booktitle={Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2012)},
year={2012},
pages={703-712},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003822207030712},
isbn={978-989-8565-02-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2012)
TI - VINCENT - Visualization of Network Centralities
SN - 978-989-8565-02-0
AU - Kerren A.
AU - Köstinger H.
AU - Zimmer B.
PY - 2012
SP - 703
EP - 712
DO - 10.5220/0003822207030712