Abstract
Visualizing dynamic networks is a challenging task. One of the challenges we face is how to maintain visual complexity and overall quality of visualizations at a reasonable and sustainable level so that the information about the network embedded in the visualization can be effectively comprehended by the viewer. Many techniques and algorithms have been proposed and developed to facilitate the discovery of changing patterns. Much research has also been done in investigating how visualization should be constructed to be effective. However, how to measure and compare the quality of visualizations of a changing network at different time points has not been well researched. In this paper, we report on a preliminary work towards this direction. In particular, we apply an existing multi-dimensional overall quality measure in a user study data of different networks and found that the measured quality is positively correlated with user task performance regardless of network size.
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References
Janicke, H., Chen, M.: A salience-based quality metric for visualization. In: Proceedings ofthe 12th Eurographics/IEEE - VGTC Conference on Visualization (EuroVis 2010), pp. 1183–1192 (2010)
Eades, P., Hong, S.-H., Klein, K., Nguyen, A.: Shape-based quality metrics for large graph visualization. In: Di Giacomo, E., Lubiw, A. (eds.) GD 2015. LNCS, vol. 9411, pp. 502–514. Springer, Heidelberg (2015). doi:10.1007/978-3-319-27261-0_41
Huang, W., Huang, M.L., Lin, C.-C.: Evaluating overall quality of graph visualizations based on aesthetics aggregation. Inf. Sci. 330, 444–454 (2016)
Huang, W., Eades, P., Hong, S.-H.: Measuring effectiveness of graph visualizations: a cognitive load perspective. Inf. Vis. 8(3), 139–152 (2009)
Huang, W., Eades, P., Hong, S.-H., Lin, C.-C.: Improving multiple aesthetics produces better graph drawings. J. Vis. Lang. Comput. 24(4), 262–272 (2013)
Friedrich, C., Eades, P.: Graph drawing in motion. JGAA 6(3), 353–370 (2002)
Moody, J., McFarland, D., Bender-DeMoll, S.: Dynamic network visualization. Am. J. Sociol. 110, 1206–1241 (2005)
Bender-deMoll, S., McFarland, D.: The art and science of dynamic network visualization. J. Soc. Struct. 7(2) (2006)
Misue, K., Eades, P., Lai, W., Sugiyama, K.: Layout adjustment and the mental map. J. Vis. Lang. Comput. 6(2), 183–210 (1995)
Minamoto, T., Shipstead, Z., Osaka, N., Engle, R.: Low cognitive load strengthens distractor interference while high load attenuates when cognitive load and distractor possess similar visual characteristics. Attention Percept. Psychophys. 77(5), 1659–1673 (2015)
Archambault, D., Purchase, H.: The map in the mental map: experimental results in dynamic graph drawing. Int. J. Hum. Comput. Stud. 71(11), 1044–1055 (2013)
Diehl, S., Gorg, C., Kerren, A.: Preserving the mental map using foresighted layout. In: VisSym, pp. 175–184 (2001)
di Battista, G., Garg, A., Liotta, G., Tamassia, R., Tassinari, E., Vargiu, F.: An experimental comparison of four graph drawing algorithms. Comput. Geom. Theory Appl. 7(5–6), 303–325 (1997)
Ng, H.K., Kalyuga, S., Sweller, J.: Reducing transience during animation: a cognitive load perspective. Educ. Psychol. 33(7), 755–772 (2013)
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Huang, W., Zhu, M., Huang, M.L., Duh, H.BL. (2016). Evaluating Overall Quality of Dynamic Network Visualizations. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2016. Lecture Notes in Computer Science(), vol 9929. Springer, Cham. https://doi.org/10.1007/978-3-319-46771-9_21
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DOI: https://doi.org/10.1007/978-3-319-46771-9_21
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