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How Users Transform Node-Link Diagrams to Matrices and Vice Versa

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Diagrammatic Representation and Inference (Diagrams 2018)

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Abstract

A combination of node-link diagram and matrix seems to be beneficial since their respective strengths and weaknesses complement each other. However, users have to read both representations in different ways and switch between these representation styles. We conducted a user study to understand how users transform a node-link diagram to a matrix representation and vice versa. For this purpose we let participants draw node-link diagrams and matrices. The drawings were analyzed to identify strategies how user convert one visualization into the other one.

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References

  1. Alper, B., Bach, B., Henry Riche, N., Isenberg, T., Fekete, J.D.: Weighted graph comparison techniques for brain connectivity analysis. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2013)

    Google Scholar 

  2. Ballweg, K., Pohl, M., Wallner, G., von Landesberger, T.: Visual similarity perception of directed acyclic graphs: a study on influencing factors. In: Frati, F., Ma, K.-L. (eds.) GD 2017. LNCS, vol. 10692, pp. 241–255. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73915-1_20

    Chapter  MATH  Google Scholar 

  3. Beck, F., Burch, M., Diehl, S., Weiskopf, D.: The state of the art in visualizing dynamic graphs. In: EuroVis State-of-The-Art Report. The Eurographics Association (2014)

    Google Scholar 

  4. Buxton, B.: Sketching User Experiences: Getting the Design Right and the Right Design. Morgan Kaufmann Publishers Inc., Burlington (2007)

    Google Scholar 

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

    Google Scholar 

  6. Henry, N., Fekete, J.D.: MatrixExplorer: a dual-representation system to explore social networks. TVCG 12(5) (2006)

    Google Scholar 

  7. Henry, N., Fekete, J.-D.: MatLink: enhanced matrix visualization for analyzing social networks. In: Baranauskas, C., Palanque, P., Abascal, J., Barbosa, S.D.J. (eds.) INTERACT 2007. LNCS, vol. 4663, pp. 288–302. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74800-7_24

    Chapter  Google Scholar 

  8. Henry, N., Fekete, J.D., McGuffin, M.J.: NodeTrix: a hybrid visualization of social networks. TVCG 13(6) (2007)

    Google Scholar 

  9. Keller, R., Eckert, C.M., Clarkson, P.J.: Matrices or node-link diagrams: which visual representation is better for visualising connectivity models? Inf. Vis. 5(1) (2006)

    Google Scholar 

  10. Ko, S., Afzal, S., Walton, S., Yang, Y., Chae, J., Malik, A., Jang, Y., Chen, M., Ebert, D.: Analyzing high-dimensional multivariate network links with integrated anomaly detection, highlighting and exploration. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology. IEEE (2014)

    Google Scholar 

  11. Liu, X., Shen, H.W.: The effects of representation and juxtaposition on graphical perception of matrix visualization. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM (2015)

    Google Scholar 

  12. Metzger, W.: Laws of Seeing. The MIT Press, Cambridge (2006)

    Book  Google Scholar 

  13. Purchase, H.C.: Metrics for graph drawing aesthetics. JVLC 13(5) (2002)

    Google Scholar 

  14. Seidler, P., Haider, J., Kodagoda, N., Wong, B.L.W., Pohl, M., Adderley, R.: Design for intelligence analysis of complex systems: evolution of criminal networks. In: Proceedings of the European Intelligence and Security Informatics Conference. IEEE (2016)

    Google Scholar 

  15. Tversky, B., Suwa, M.: Thinking with Sketches. Oxford University Press, Oxford (2009)

    Book  Google Scholar 

  16. Walny, J., Huron, S., Carpendale, S.: An Exploratory Study of Data Sketching for Visual Representation. In: Computer Graphics Forum (2015)

    Google Scholar 

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Acknowledgments

This work was supported by CVAST, funded by the Austrian Federal Ministry of Science, Research, and Economy in the exceptional Laura Bassi Centres of Excellence initiative (#822746).

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Correspondence to Simone Kriglstein .

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Kriglstein, S., Pohl, M., Doppler Haider, J. (2018). How Users Transform Node-Link Diagrams to Matrices and Vice Versa. In: Chapman, P., Stapleton, G., Moktefi, A., Perez-Kriz, S., Bellucci, F. (eds) Diagrammatic Representation and Inference. Diagrams 2018. Lecture Notes in Computer Science(), vol 10871. Springer, Cham. https://doi.org/10.1007/978-3-319-91376-6_48

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  • DOI: https://doi.org/10.1007/978-3-319-91376-6_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91375-9

  • Online ISBN: 978-3-319-91376-6

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