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Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery pp 459–483Cite as

Integrating Visual Exploration and Direct Editing of Multivariate Graphs

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 1014))

Abstract

A central concern of analyzing multivariate graphs is to study the relation between the graph structure and its multivariate attributes. During the analysis, it can also be relevant to edit the graph data, for example, to correct identified errors, update outdated information, or to experiment with what-if scenarios, that is, to study the influence of certain attribute values on the graph. To facilitate both, the visual exploration and direct editing of multivariate graphs, we propose a novel interactive visualization approach. The core idea is to show the graph structure and calculated attribute similarity in an integrated fashion as a matrix. A table can be attached to the matrix on demand to visualize the graph’s multivariate attributes. To support the visual comparison of structure and attributes at different levels, several mechanisms are provided, including matrix reordering, selection and emphasis of subsets, rearrangement of sub-matrices, and column rotation for detailed comparison. Integrated into the visualization are interaction techniques that enable users to directly edit the graph data and observe the resulting changes on the fly. Overall, we present a novel integrated approach to explore relations between structure and attributes, to edit the graph data, and to investigate changes in the characteristics of their relationships. To demonstrate the utility of the presented solution, we apply it to explore and edit structure and attributes of a network of soccer players.

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Notes

  1. 1.

    Note that this chapter is an extended version of a previous paper [5] While the original paper focused solely on the visual exploration of multivariate graphs, this extended chapter adds the direct editing part.

References

  1. Pretorius, J., Purchase, H.C., Stasko, J.T.: Tasks for multivariate network analysis. In: Multivariate Network Visualization, pp. 77–95. Springer International Publishing (2014)

    Google Scholar 

  2. Nobre, C., Meyer, M., Streit, M., Lex, A.: The State of the art in visualizing multivariate networks. Comput. Graph. Forum 38(3), 807–832 (2019)

    Article  Google Scholar 

  3. Baudel, T.: From information visualization to direct manipulation: extending a generic visualization framework for the interactive editing of large datasets. In: Proceedings of ACM Symposium on User Interface Software and Technology. ACM (2006)

    Google Scholar 

  4. Kandel, S., Heer, J., Plaisant, C., Kennedy, J., Van Ham, F., Riche, N.H., Weaver, C., Lee, B., Brodbeck, D., Buono, P.: Research Directions in data wrangling: visualizations and transformations for usable and credible data. Inf. Vis. 10(4), 271–288 (2011)

    Article  Google Scholar 

  5. Berger, P., Schumann, H., Tominski, C.: Visually exploring relations between structure and attributes in multivariate graphs. In: Proceedings of IEEE International Conference on Information Visualization. IEEE (2019)

    Google Scholar 

  6. Tominski, C.: Schumann, H.: Interactive Visual Data Analysis. CRC Press, AK Peters Visualization Series (2020)

    Google Scholar 

  7. Ward, M.O., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications, 2 edn. A K Peters/CRC Press (2015)

    Google Scholar 

  8. Tamassia, R. (ed.): Handbook of Graph Drawing and Visualization. CRC Press (2013)

    Google Scholar 

  9. Behrisch, M., Bach, B., Henry Riche, N., Schreck, T., Fekete, J.D.: Matrix reordering methods for table and network visualization. Comput. Graph. Forum 35(3), 693–716 (2016)

    Article  Google Scholar 

  10. Kerren, A., Purchase, H.C., Ward, M.O. (eds.): Multivariate Network Visualization. Springer International Publishing (2014)

    Google Scholar 

  11. Shannon, R., Holland, T., Quigley, A.: Multivariate Graph Drawing Using Parallel Coordinate Visualisations. University College Dublin, School of Computer Science and Informatics (2008)

    Google Scholar 

  12. Lex, A., Streit, M., Kruijff, E., Schmalstieg, D.: Caleydo: design and evaluation of a visual analysis framework for gene expression data in its biological context. In: Proceedings of IEEE Pacific Symposium on Visualization, pp. 57–64 (2010)

    Google Scholar 

  13. Partl, C., Lex, A., Streit, M., Kalkofen, D., Kashofer, K., Schmalstieg, D.: enRoute: dynamic path extraction from biological pathway maps for in-depth experimental data analysis. In: Proceedings of IEEE Symposium on Biological Data Visualization, pp. 107–114. IEEE (2012)

    Google Scholar 

  14. Partl, C., Gratzl, S., Streit, M., Wassermann, A.M., Pfister, H., Schmalstieg, D., Lex, A.: Pathfinder: visual analysis of paths in graphs. Comput. Graph Forum 35(3), 71–80 (2016)

    Article  Google Scholar 

  15. Cao, N., Lin, Y.R., Li, L., Tong, H.: g-Miner: interactive visual group mining on multivariate graphs. In: Proceedings of ACM Conference on Human Factors in Computing Systems, pp. 279–288. ACM (2015)

    Google Scholar 

  16. Van den Elzen, S., Van Wijk, J.J.: Multivariate network exploration and presentation: from detail to overview via selections and aggregations. IEEE Trans. Vis. Comput. Graph. 20(12), 2310–2319 (2014)

    Article  Google Scholar 

  17. Shneiderman, B., Aris, A.: Network visualization by semantic substrates. IEEE Trans. Vis. Comput. Graph. 12(5), 733–740 (2006)

    Article  Google Scholar 

  18. Rodrigues, E.M., Milic-Frayling, N., Smith, M., Shneiderman, B., Hansen, D.: Group-in-a-box layout for multi-faceted analysis of communities. In: Proceedings of IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pp. 354–361. IEEE (2011)

    Google Scholar 

  19. Wattenberg, M.: Visual exploration of multivariate graphs. In: Proceedings of ACM Conference on Human Factors in Computing Systems, pp. 811–819. ACM (2006)

    Google Scholar 

  20. Bezerianos, A., Chevalier, F., Dragicevic, P., Elmqvist, N., Fekete, J.D.: Graphdice: a system for exploring multivariate social networks. Comput. Graph. Forum 29(3), 863–872 (2010)

    Article  Google Scholar 

  21. Eichner, C., Gladisch, S., Schumann, H., Tominski, C.: Direct visual editing of node attributes in graphs. Informatics 3(4), 17 (2016)

    Article  Google Scholar 

  22. Major, T., Basole, R.C.: Graphicle: exploring units, networks, and context in a blended visualization approach. IEEE Trans. Vis. Comput. Graph. 25(1), 576–585 (2019)

    Article  Google Scholar 

  23. Nobre, C., Streit, M., Lex, A.: Juniper: a tree+table approach to multivariate graph visualization. IEEE Trans. Vis. Comput. Graph. 25(1), 544–554 (2019)

    Article  Google Scholar 

  24. Bastian, M., Heymann, S., Jacomy, M.: An Open Source Software for Exploring and Manipulating Networks. The AAAI Press (2009)

    Google Scholar 

  25. Auber, D.: Tulip—a huge graph visualization framework. In: Graph Drawing Software, pp. 105–126. Springer International Publishing (2004)

    Google Scholar 

  26. Gladisch, S., Schumann, H., Ernst, M., Füllen, G., Tominski, C.: Semi-automatic editing of graphs with customized layouts. Comput. Graph. Forum 33(3), 381–390 (2014)

    Article  Google Scholar 

  27. Gladisch, S., Schumann, H., Loboschik, M., Tominski, C.: Toward using matrix visualizations for graph editing. Poster at IEEE Conference on Information Visualization (2015)

    Google Scholar 

  28. Kister, U., Klamka, K., Tominski, C.: Dachselt, R: GraSp: combining spatially-aware mobile devices and a display wall for graph visualization and interaction. Comput. Graph. Forum 36, 503–514 (2017)

    Article  Google Scholar 

  29. Horak, T., Berger, P., Schumann, H., Dachselt, R., Tominski, C.: Responsive matrix cells: a focus+context approach for exploring and editing multivariate graphs. IEEE Trans. Vis. Comput. Graph. 27(2), 1644–1654 (2021)

    Article  Google Scholar 

  30. Ghoniem, M., Fekete, J.D., Castagliola, P.: A comparison of the readability of graphs using node-link and matrix-based representations. In: Proceedings of IEEE Symposium on Information Visualization (2004)

    Google Scholar 

  31. Behrisch, M., Davey, J., Fischer, F., Thonnard, O., Schreck, T., Keim, D., Kohlhammer, J.: Visual analysis of sets of heterogeneous matrices using projection-based distance functions and semantic zoom. Comput. Graph. Forum 33(3), 411–420 (2014)

    Article  Google Scholar 

  32. Kohonen, T.: Self-organizing Maps. Springer Science & Business Media (2012)

    Google Scholar 

  33. Harrower, M.A., Brewer, C.A.: ColorBrewer.org: an online tool for selecting color schemes for maps. Cartographic J. 40(1), 27–37 (2003)

    Google Scholar 

  34. John, M., Tominski, C., Schumann, H.: Visual and analytical extensions for the table lens. In: Proceedings of SPIE Conference on Visualization and Data Analysis, pp. 680907–1–680907–12. SPIE (2008)

    Google Scholar 

  35. Tominski, C., Forsell, C., Johansson, J.: Interaction Support for Visual Comparison Inspired by Natural Behavior. IEEE Trans. Vis. Comput. Graph. 18(12), 2719–2728 (2012)

    Article  Google Scholar 

  36. Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C.D., Roberts, J.C.: Visual comparison for information visualization. Inf. Vis. 10(4), 289–309 (2011)

    Article  Google Scholar 

  37. 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)

    Article  Google Scholar 

  38. Henry, N., Fekete, J.D.: Matlink: enhanced matrix visualization for analyzing social networks. In: Proceedings of IFIP Conference on Human-Computer Interaction, pp. 288–302. Springer International Publishing (2007)

    Google Scholar 

  39. Tominski, C.: CompaRing: reducing costs of visual comparison. In: Short Paper Proceedings of the IEEE VGTC/Eurographics Conference on Visualization. Eurographics Association (2016)

    Google Scholar 

  40. Abello, J., Van Ham, F.: Matrix zoom: a visual interface to semi-external graphs. In: Proceedings of IEEE Symposium on Information Visualization, pp. 183–190 (2004)

    Google Scholar 

  41. Jolliffe, I.T.: Principal Components in Regression Analysis, pp. 129–155. Springer International Publishing, Cham, Switzerland (1986)

    Google Scholar 

  42. Van der Maaten, L., Hinton, G.: Visualizing Data using t-SNE. J. Mach. Learn. Res. 9(86), 2579–2605 (2008)

    MATH  Google Scholar 

  43. Chegini, M., Bernard, J., Berger, P., Sourin, A., Andrews, K., Schreck, T.: Interactive labelling of a multivariate dataset for supervised machine learning using linked visualisations, clustering, and active learning. Vis. Inf. 3(1), 9–17 (2019)

    Google Scholar 

  44. Kreuseler, M., Nocke, T., Schumann, H.: A history mechanism for visual data mining. In: Proceedings of IEEE Symposium on Information Visualization, pp. 49–56. IEEE (2004)

    Google Scholar 

  45. Mathisen, A., Horak, T., Klokmose, C.N., Grønbæk, K., Elmqvist, N.: Integrating data-driven reporting in collaborative visual analytics. Comput. Graph. Forum, InsideInsights (2019)

    Book  Google Scholar 

  46. Nancel, M., Cockburn, A.: Causality: a conceptual model of interaction history. In: Proceedings of ACM Conference on Human Factors in Computing Systems, pp. 1777–1786. ACM (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Rostock, Rostock, Germany

    Philip Berger, Heidrun Schumann & Christian Tominski

Authors
  1. Philip Berger
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  2. Heidrun Schumann
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  3. Christian Tominski
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Corresponding author

Correspondence to Philip Berger .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Central Washington University, Ellensburg, WA, USA

    Boris Kovalerchuk

  2. Department of Media, Darmstadt University of Applied Sciences, Darmstadt, Hessen, Germany

    Kawa Nazemi

  3. Department of Computer Science, Central Washington University, Ellensburg, WA, USA

    Răzvan Andonie

  4. Department of Electronics, Telecommunications and Computers Engineering, Lisbon School of Engineering, Lisbon, Portugal

    Nuno Datia

  5. Department of Informatics, London South Bank University, London, UK

    Ebad Banissi

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Berger, P., Schumann, H., Tominski, C. (2022). Integrating Visual Exploration and Direct Editing of Multivariate Graphs. In: Kovalerchuk, B., Nazemi, K., Andonie, R., Datia, N., Banissi, E. (eds) Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. Studies in Computational Intelligence, vol 1014. Springer, Cham. https://doi.org/10.1007/978-3-030-93119-3_18

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