Interactive Network Exploration to Derive Insights: Filtering, Clustering, Grouping, and Simplification

  • Ben Shneiderman
  • Cody Dunne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7704)

Abstract

The growing importance of network analysis has increased attention on interactive exploration to derive insights and support personal, business, legal, scientific, or national security decisions. Since networks are often complex and cluttered, strategies for effective filtering, clustering, grouping, and simplification are helpful in finding key nodes and links, surprising clusters, important groups, or meaningful patterns. We describe readability metrics and strategies that have been implemented in NodeXL, our free and open source network analysis tool, and show examples from our research. While filtering, clustering, and grouping have been used in many tools, we present several advances on these techniques. We also discuss our recent work on motif simplification, in which common patterns are replaced with compact and meaningful glyphs, thereby improving readability.

Keywords

Network visualization visual analytics readability metrics dynamic filters link clustering attribute grouping motif simplification 

References

  1. 1.
    Ahlberg, C., Williamson, C., Shneiderman, B.: Dynamic queries for information exploration: an implementation and evaluation. In: CHI 1992: Proc. SIGCHI Conference on Human Factors in Computing Systems, pp. 619–626 (1992), doi:10.1145/142750.143054Google Scholar
  2. 2.
    Bonsignore, E.M., Dunne, C., Rotman, D., Smith, M., Capone, T., Hansen, D.L., Shneiderman, B.: First steps to NetViz Nirvana: Evaluating social network analysis with NodeXL. In: CSE 2009: Proc. 2009 International Conference on Computational Science and Engineering, vol. 4, pp. 332–339 (2009), doi:10.1109/CSE.2009.120Google Scholar
  3. 3.
    Bruls, M., Huizing, K., Van Wijk, J.J.: Squarified Treemaps. In: Proc. Joint Eurographics and IEEE TCVG Symposium on Visualization, pp. 33–42 (2000)Google Scholar
  4. 4.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics 70, 066111 (2004), doi:10.1103/PhysRevE.70.066111Google Scholar
  5. 5.
    Davidson, R., Harel, D.: Drawing graphs nicely using simulated annealing. TOG: ACM Transactions on Graphics 15(4), 301–331 (1996), doi:10.1145/234535.234538CrossRefGoogle Scholar
  6. 6.
    Dunne, C., Riche, N.H., Lee, B., Metoyer, R.A., Robertson, G.G.: GraphTrail: Analyzing large multivariate, heterogeneous networks while supporting exploration history. In: CHI 2012: Proc. 2012 International Conference on Human Factors in Computing Systems, pp. 1663–1672 (2012), doi:10.1145/2207676.2208293Google Scholar
  7. 7.
    Dunne, C., Shneiderman, B.: Improving graph drawing readability by incorporating readability metrics: A software tool for network analysts. Human-Computer Interaction Lab Tech Report HCIL-2009-13, University of Maryland (2009)Google Scholar
  8. 8.
    Dunne, C., Shneiderman, B.: Motif simplification: Improving network visualization readability with fan and parallel glyphs. Human-Computer Interaction Lab Tech Report HCIL-2012-11, University of Maryland (2012)Google Scholar
  9. 9.
    Dunne, C., Shneiderman, B., Gove, R., Klavans, J., Dorr, B.: Rapid understanding of scientific paper collections: Integrating statistics, text analytics, and visualization. JASIST: Journal of the American Society for Information Science and Technology (2012)Google Scholar
  10. 10.
    Eades, P.: A heuristic for graph drawing. CN: Congressus Numerantium 42, 149–160 (1984)MathSciNetGoogle Scholar
  11. 11.
    Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. SPE: Software: Practice and Experience 21(11), 1129–1164 (1991), doi:10.1002/spe.4380211102CrossRefGoogle Scholar
  12. 12.
    Gansner, E.R., Hu, Y.: Efficient Node Overlap Removal Using a Proximity Stress Model. In: Tollis, I.G., Patrignani, M. (eds.) GD 2008. LNCS, vol. 5417, pp. 206–217. Springer, Heidelberg (2009), doi:10.1007/978-3-642-00219-9_20CrossRefGoogle Scholar
  13. 13.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS: Proc. National Academy of Sciences of the United States of America 99(12), 7821–7826 (2002), doi:10.1073/pnas.122653799MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    Hachul, S., Jünger, M.: An Experimental Comparison of Fast Algorithms for Drawing General Large Graphs. In: Healy, P., Nikolov, N.S. (eds.) GD 2005. LNCS, vol. 3843, pp. 235–250. Springer, Heidelberg (2006), doi:10.1007/11618058_22CrossRefGoogle Scholar
  15. 15.
    Hansen, D., Shneiderman, B., Smith, M.: Analyzing social media networks with NodeXL: Insights from a connected world. Morgan Kaufmann (2011)Google Scholar
  16. 16.
    Harel, D., Koren, Y.: A fast multi-scale method for drawing large graphs. JGAA: Journal of Graph Algorithms and Applications 6(3), 179–202 (2002)MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Harel, D., Koren, Y.: Drawing graphs with non-uniform vertices. In: AVI 2002: Proc. Working Conference on Advanced Visual Interfaces, pp. 157–166 (2002), doi:10.1145/1556262.1556288Google Scholar
  18. 18.
    Harel, D., Koren, Y.: Graph Drawing by High-Dimensional Embedding. In: Goodrich, M.T., Kobourov, S.G. (eds.) GD 2002. LNCS, vol. 2528, pp. 207–219. Springer, Heidelberg (2002), doi:10.1007/3-540-36151-0_20CrossRefGoogle Scholar
  19. 19.
    Holten, D.: Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. TVCG: IEEE Transactions on Visualization and Computer Graphics 12(5), 741–748 (2006), doi:10.1109/TVCG.2006.147CrossRefGoogle Scholar
  20. 20.
    Huang, W., Hong, S.-H., Eades, P.: Layout Effects on Sociogram Perception. In: Healy, P., Nikolov, N.S. (eds.) GD 2005. LNCS, vol. 3843, pp. 262–273. Springer, Heidelberg (2006), doi:10.1007/11618058_24CrossRefGoogle Scholar
  21. 21.
    Lam, H., Bertini, E., Isenberg, P., Plaisant, C., Carpendale, S.: Empirical studies in information visualization: Seven scenarios. TVCG: IEEE Transactions on Visualization and Computer Graphics PP(99), 1 (2011), doi:10.1109/TVCG.2011.279Google Scholar
  22. 22.
    McGrath, C., Blythe, J., Krackhardt, D.: The effect of spatial arrangement on judgments and errors in interpreting graphs. SN: Social Networks 19(3), 223–242 (1997), doi:10.1016/S0378-8733(96)00299-7CrossRefGoogle Scholar
  23. 23.
    North, C.: Toward measuring visualization insight. CGA: IEEE Computer Graphics and Applications 26(3), 6–9 (2006), doi:10.1109/MCG.2006.70CrossRefGoogle Scholar
  24. 24.
    Perer, A., Shneiderman, B.: Systematic yet flexible discovery: Guiding domain experts through exploratory data analysis. In: IUI 2008: Proc. 13th International Conference on Intelligent User Interfaces, pp. 109–118 (2008), doi:10.1145/1378773.1378788Google Scholar
  25. 25.
    Perer, A., Shneiderman, B.: Integrating statistics and visualization for exploratory power: From long-term case studies to design guidelines. CGA: IEEE Computer Graphics and Applications 29(3), 39–51 (2009), doi:10.1109/MCG.2009.44CrossRefGoogle Scholar
  26. 26.
    Pupyrev, S., Nachmanson, L., Bereg, S., Holroyd, A.E.: Edge Routing with Ordered Bundles. In: van Kreveld, M., Speckmann, B. (eds.) GD 2011. LNCS, vol. 7034, pp. 136–147. Springer, Heidelberg (2012), doi:10.1007/978-3-642-25878-7_14Google Scholar
  27. 27.
    Purchase, H.C.: Metrics for graph drawing aesthetics. JVLC: Journal of Visual Languages & Computing 13, 501–516 (2002), doi:10.1006/jvlc.2002.0232CrossRefGoogle Scholar
  28. 28.
    Rodrigues, E.M., Milic-Frayling, N., Smith, M., Shneiderman, B., Hansen, D.: Group-in-a-Box layout for multi-faceted analysis of communities. In: SocialCom 2011: Proc. 2011 IEEE 3rd International Conference on Social Computing, pp. 354–361 (2011), doi:10.1109/PASSAT/SocialCom.2011.139Google Scholar
  29. 29.
    Shneiderman, B.: Inventing Discovery Tools: Combining Information Visualization with Data Mining. In: Jantke, K.P., Shinohara, A. (eds.) DS 2001. LNCS (LNAI), vol. 2226, pp. 17–28. Springer, Heidelberg (2001), doi:10.1007/3-540-45650-3_4CrossRefGoogle Scholar
  30. 30.
    Shneiderman, B., Plaisant, C.: Strategies for evaluating information visualization tools: Multi-dimensional in-depth long-term case studies. In: BELIV 2006: Proc. 2006 AVI Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization, pp. 1–7 (2006), doi:10.1145/1168149.1168158Google Scholar
  31. 31.
    Smith, M., Shneiderman, B., Milic-Frayling, N., Rodrigues, E.M., Barash, V., Dunne, C., Capone, T., Perer, A., Gleave, E.: Analyzing (social media) networks with NodeXL. In: C&T 2009: Proc. Fourth International Conference on Communities and Technologies, pp. 255–264 (2009), doi:10.1145/1556460.1556497Google Scholar
  32. 32.
    Sugiyama, K.: Graph drawing and applications for software and knowledge engineers, vol. 11. World Scientific Publishing Company (2002)Google Scholar
  33. 33.
    Wakita, K., Tsurumi, T.: Finding community structure in mega-scale social networks: [extended abstract]. In: WWW 2007: Proc. 16th International Conference on World Wide Web, pp. 1275–1276 (2007), doi:10.1145/1242572.1242805Google Scholar
  34. 34.
    Wattenberg, M.: Visual exploration of multivariate graphs. In: CHI 2006: Proc. SIGCHI Conference on Human Factors in Computing Systems, pp. 811–819 (2006), doi:10.1145/1124772.1124891Google Scholar
  35. 35.
    Williamson, C., Shneiderman, B.: The dynamic HomeFinder: evaluating dynamic queries in a real-estate information exploration system. In: SIGIR 1992: Proc. 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 338–346 (1992), doi:10.1145/133160.133216Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ben Shneiderman
    • 1
  • Cody Dunne
    • 1
  1. 1.University of MarylandCollege ParkUSA

Personalised recommendations