Skip to main content
SpringerLink
Log in

Pixel-Oriented Network Visualization: Static Visualization of Change in Social Networks

  • Chapter
  • First Online:
The Influence of Technology on Social Network Analysis and Mining

Part of the book series: Lecture Notes in Social Networks ((LNSN,volume 6))

  • 2662 Accesses

Abstract

Most common network visualizations rely on graph drawing. While without doubt useful, graphs suffer from limitations like cluttering and important patterns may not be realized especially when networks change over time. We propose a novel approach for the visualization of user interactions in social networks: a pixel-oriented visualization of a graphical network matrix where activity timelines are folded to inner glyphs within each matrix cell. Users are ordered by similarity which allows to uncover interesting patterns. The visualization is exemplified using social networks based on corporate wikis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This chapter is based on a conference paper at ASONAM 2010.

  2. 2.

    See [32] for a detailed discussion of this measure.

  3. 3.

    For example UCINET (http://www.analytictech.com/ucinet/), JUNG (http://jung.sourceforge.net/), Graphviz (http://www.graphviz.org/), GUESS (http://graphexploration.cond.org/), Pajek (http://pajek.imfm.si/), Visone (http://visone.info/), and others. See also http://www.google.com/Top/Science/Math/Combinatorics/Software/Graph_Drawing/, http://www.graphdrawing.org/ and the INSNA software list (http://www.insna.org/software/).

  4. 4.

    http://www.stanford.edu/group/sonia/

  5. 5.

    http://sonivis.org

  6. 6.

    For an animated representation of the network shown in Fig. 5.1 see http://www.kinf.wiai.uni-bamberg.de/mwstat/examples/wiki_1_weekly_network.swf.

  7. 7.

    Bitmap of sorted adjacency matrix.

  8. 8.

    At least I hope this is visible in the printout.

  9. 9.

    Where the timeline was presented column by column.

  10. 10.

    Which could be flipped e.g. for Arabic readers who may prefer right → left.

  11. 11.

    For example CLIQUE [2]. For an overview on cluster analysis see Everitt et al. [12].

  12. 12.

    For indices outside the time range (e.g. a  − 1) the value 0 is substituted.

  13. 13.

    Which are in fact very similar, see e.g. [23].

  14. 14.

    Multidimensional scaling (MDS, [21]) in several variants [10].

  15. 15.

    POVNA [35] is written in Java. Not all features described are included in the current stable release.

  16. 16.

    In-depth case studies of the wikis used here including other types of (not pixel-oriented) visualizations are provided by Stein and Blaschke [31].

  17. 17.

    This is less a problem in interactive usage where we can scroll.

  18. 18.

    We may also see that they never edited the wiki on weekends.

  19. 19.

    Wiki Explorator is written in Ruby using R, Gnuplot, Graphviz and other open source software.

  20. 20.

    http://wiki-explorator.rubyforge.org. We also provide an online wiki analysis service based on this library at http://www.kinf.wiai.uni-bamberg.de/mwstat.

References

  1. Aigner, W., Miksch, S., Müller, W., Schumann, H., Tominski, C.: Visualizing time-oriented data, a systematic view. Comput. Graph. 31(3), 401–409 (2007)

    Article  Google Scholar 

  2. Ankerst, M., Berchtold, S., Keim, D.A.: Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In: INFOVIS ’98: Proceedings of the 1998 IEEE Symposium on Information Visualization. IEEE Computer Society, Washington, DC (1998)

    Google Scholar 

  3. Balzer, M., Deussen, O.: Level-of-detail visualization of clustered graph layouts. In: APVIS’07, pp. 133–140. IEEE, Piscataway (2007)

    Google Scholar 

  4. Bender-deMoll, S., McFarland, D.A.: The art and science of dynamic network visualization. J. Soc. Struct. 7(2), 1–46 (2006). http://www.cmu.edu/joss/content/articles/volume7/deMollMcFarland

    Google Scholar 

  5. Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is “nearest neighbor” meaningful? In: Database Theory—ICDT’99, pp. 217–235. Springer, Berlin/New York (1999)

    Google Scholar 

  6. Brandes, U., Kenis, P., Raab, J.: Explanation through network visualization. Methodology 2(1), 16–23 (2006)

    Google Scholar 

  7. Chen, C.: Information Visualization: Beyond the Horizon, 2nd edn. Springer, London (2006)

    Google Scholar 

  8. Chen, I.X., Yang, C.Z.: Visualization of social networks. In: Furht, B. (ed.) Handbook of Social Network Technologies and Applications, pp. 585–610. Springer, New York (2010)

    Chapter  Google Scholar 

  9. Chen, W., Ji, M., Tang, X., Zhang, B., Shi, B.: Visualization tools for exploring social networks and travel behavior. In: 2010 International Conference on Environmental Science and Information Application Technology (ESIAT), vol. 3, pp. 239–243. IEEE, Piscataway (2010)

    Google Scholar 

  10. Cox, M.A.A., Cox, T.F.: Multidimensional scaling. In: Chen, C.h., Härdle, W., Unwin, A. (eds.) Handbook of Data Visualization. Springer Handbooks Computational Statistics, pp. 315–347. Springer, Berlin/Heidelberg (2008). doi:http://dx.doi.org/10.1007/978-3-540-33037-0_14

  11. Eppstein, D., Gansner, E.R. (eds.): Graph Drawing: 17th International Symposium, GD 2009. LNCS, vol. 5849. Springer, Berlin/New York (2010)

    Google Scholar 

  12. Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 4th edn. Wiley, Chichester (2009)

    Google Scholar 

  13. Ghoniem, M., Fekete, J.D., Castagliola, P.: A comparison of the readability of graphs using node-link and matrix-based representations. In: INFOVIS ’04: Proceedings of the IEEE Symposium on Information Visualization, pp. 17–24. IEEE, Washington, DC (2004). doi:http://dx.doi.org/10.1109/INFOVIS.2004.1

  14. Guo, Y., Chen, C., Zhou, S.: Fingerprint for network topologies. In: Zhou, J. (ed.) Complex Sciences, pp. 1666–1677. Springer, Berlin/Heidelberg (2009)

    Chapter  Google Scholar 

  15. Henry, N., Fekete, J.D., McGuffin, M.J.: Nodetrix: a hybrid visualization of social networks. IEEE Trans. Vis. Comput. Graph. 13(6), 1302–1309 (2007). doi:http://dx.doi.org/10.1109/TVCG.2007.70582

    Google Scholar 

  16. Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Inf. Process. Lett. 31(1), 7–15 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  17. Keim, D.A.: Designing pixel-oriented visualization techniques: theory and applications. IEEE Trans. Vis. Comput. Graph. 6(1), 59–78 (2000). doi:http://dx.doi.org/10.1109/2945.841121

  18. Keim, D.A., Keim, D.A., Kriegel, H.P., Kriegel, H.P.: Visdb: database exploration using multidimensional visualization. IEEE Comput. Graph. Appl. 14, 40–49 (1994)

    Article  Google Scholar 

  19. Keim, D.A., Ankerst, M., Kriegel, H.P.: Recursive pattern: a technique for visualizing very large amounts of data. In: VIS ’95: Proceedings of the 6th Conference on Visualization ’95. IEEE Computer Society, Washington, DC (1995)

    Google Scholar 

  20. Krempel, L.: Network visualization. In: Scott, J., Carrington, P.J. (eds.) Sage Handbook of Social Network Analysis. SAGE, London/Thousand Oaks (2009)

    Google Scholar 

  21. Kruskal, J.B., Wish, M.: Multidimensional Scaling. Sage University Paper Series on Quantitative Application in the Social Sciences. Sage, Beverly Hills/London (1978)

    Google Scholar 

  22. Leung, C., Carmichael, C.: Exploring social networks: a frequent pattern visualization approach. In: IEEE International Conference on Social Computing/IEEE International Conference on Privacy, Security, Risk and Trust, pp. 419–424. IEEE, Los Alamitos (2010)

    Google Scholar 

  23. Leydesdorff, L.: On the normalization and visualization of author co-citation data: Salton’s cosine versus the jaccard index. J. Am. Soc. Inf. Sci. Technol. 59(1), 77–85 (2008)

    Article  Google Scholar 

  24. Moody, J., McFarland, D., Bender-deMoll, S.: Dynamic network visualization. Am. J. Sociol. 110(4), 1206–1241 (2005). doi:10.1086/421509

    Article  Google Scholar 

  25. Peng, W., SiKun, L.: Social network visualization via domain ontology. In: International Conference on Information Engineering and Computer Science, pp. 1–4. IEEE, Piscataway (2009)

    Google Scholar 

  26. Reeve, L., Han, H., Chen, C.: Information visualization and the semeantic web. In: Geroimenko, V., Chen, C. (eds.) Visualizing the Semantic Web: XML-Based Internet and Information Visualization. Springer, London (2006)

    Google Scholar 

  27. Riche, N.H., Fekete, J.D.: Novel visualizations and interactions for social networks exploration. In: Furht, B. (ed.) Handbook of Social Network Technologies and Applications, pp. 611–636. Springer, New York (2010). doi:http://dx.doi.org/10.1007/978-1-4419-7142-5_28

  28. Shi, L., Cao, N., Liu, S., Qian, W., Tan, L., Wang, G., Sun, J., Lin, C.: HiMap: adaptive visualization of large-scale online social networks. In: Visualization Symposium, PacificVis ’09. IEEE, Piscataway (2009)

    Google Scholar 

  29. Shneiderman, B.: Extreme visualization: squeezing a billion records into a million pixels. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 3–12. ACM, New York (2008)

    Google Scholar 

  30. Shneiderman, B., Aris, A.: Network visualization by semantic substrates. IEEE Trans. Vis. Comput. Graph. 12(5), 733–740 (2006). doi:http://dx.doi.org/10.1109/TVCG.2006.166

    Google Scholar 

  31. Stein, K., Blaschke, S.: Corporate wikis: comparative analysis of structures and dynamics. In: Hinkelmann, K., Wache, H. (eds.) Proceedings of the 5th Conference on Professional Knowledge Management. Lecture Notes in Informatics, pp. 77–86. Gesellschaft für Informatik, Bonn (2009)

    Google Scholar 

  32. Stein, K., Blaschke, S.: Interlocking communication, measuring collaborative intensity in social networks. In: Social Networks Analysis and Mining: Foundations and Applications. Springer, New York (2010)

    Google Scholar 

  33. Unwin, A., Theus, M., Hofmann, H.: Graphics of Large Datasets: Visualizing a Million. Springer, New York (2006)

    Google Scholar 

  34. Ware, C.: Visual Queries: The Foundation of Visual Thinking. Springer, Berlin/Heidelberg (2005)

    Google Scholar 

  35. Wegener, R.: Kollaborationsprozesse in Wikis: Entwurf und Umsetzung eines Analysewerkzeugs. Master’s thesis, University of Bamberg (2009)

    Google Scholar 

Download references

Acknowledgements

Part of this work was supported by the Volkswagenstiftung through Grant No. II/82 509.

We thank the reviewers for their helpful comments.

Author information

Authors and Affiliations

  1. Computing in the Cultural Sciences, University of Bamberg, Bamberg, Germany

    Klaus Stein & Christoph Schlieder

  2. Information Systems, Kassel University, Kassel, Germany

    René Wegener

Authors
  1. Klaus Stein
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. René Wegener
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoph Schlieder
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Klaus Stein .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, TOBB University, Sogutozu Cad No. 43, Sogutozu Ankara, Turkey

    Tansel Özyer

  2. Computer Science, University of Calgary, University Dr. NW 2500, Calgary, T2N 1N4, Canada

    Jon Rokne

  3. IPSC, European Commission Joint Research Cent., Via Enrico Fermi 2749, Ispra, 21027, Italy

    Gerhard Wagner

  4. De Wetstraat 16, Leiden, 2332 XT, Netherlands

    Arno H.P. Reuser

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Wien

About this chapter

Cite this chapter

Stein, K., Wegener, R., Schlieder, C. (2013). Pixel-Oriented Network Visualization: Static Visualization of Change in Social Networks. In: Özyer, T., Rokne, J., Wagner, G., Reuser, A. (eds) The Influence of Technology on Social Network Analysis and Mining. Lecture Notes in Social Networks, vol 6. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1346-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-7091-1346-2_5

  • Published:

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-7091-1345-5

  • Online ISBN: 978-3-7091-1346-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation