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Understanding node-link and matrix visualizations of networks: A large-scale online experiment

  • Donghao Ren (a1), Laura R. Marusich (a2), John O’Donovan (a3), Jonathan Z. Bakdash (a4), James A. Schaffer (a5), Daniel N. Cassenti (a6), Sue E. Kase (a7), Heather E. Roy (a8), Wan-yi (Sabrina) Lin (a9) and Tobias Höllerer (a10)...

Abstract

We investigated human understanding of different network visualizations in a large-scale online experiment. Three types of network visualizations were examined: node-link and two different sorting variants of matrix representations on a representative social network of either 20 or 50 nodes. Understanding of the network was quantified using task time and accuracy metrics on questions that were derived from an established task taxonomy. The sample size in our experiment was more than an order of magnitude larger (N = 600) than in previous research, leading to high statistical power and thus more precise estimation of detailed effects. Specifically, high statistical power allowed us to consider modern interaction capabilities as part of the evaluated visualizations, and to evaluate overall learning rates as well as ambient (implicit) learning. Findings indicate that participant understanding was best for the node-link visualization, with higher accuracy and faster task times than the two matrix visualizations. Analysis of participant learning indicated a large initial difference in task time between the node-link and matrix visualizations, with matrix performance steadily approaching that of the node-link visualization over the course of the experiment. This research is reproducible as the web-based module and results have been made available at: https://osf.io/qct84/.

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Corresponding author

*Corresponding author. Email: donghaoren@cs.ucsb.edu

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The author is currently affiliated with BOSCH Center for Artificial Intelligence. Email: Wan-Yi.Lin@us.bosch.com

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References

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Alper, B., Bach, B., Henry Riche, N., Isenberg, T., & Fekete, J.-D. (2013). Weighted graph comparison techniques for brain connectivity analysis. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 483492). Paris, France, ACM.
Army, U. S. (2006). Field manual 2-22.3: Human intelligence collector operations.
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390412.
Baccara, M., & Bar-Isaac, H. (2008). How to organize crime. The Review of Economic Studies, 75(4), 10391067.
Bakker, M., van Dijk, A., & Wicherts, J. M. (2012). The rules of the game called psychological science. Perspectives on Psychological Science, 7(6), 543554.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 148.
Battista, G. D., Eades, P., Tamassia, R., & Tollis, I. G. (1998). Graph drawing: Algorithms for the visualization of graphs (1st ed.) Upper Saddle River, NJ, USA: Prentice Hall PTR.
Berardi, C. W, Solovey, E. T., & Cummings, M. L. (2013). Investigating the efficacy of network visualizations for intelligence tasks. In 2013 IEEE international conference on intelligence and security informatics (ISI) (pp. 278283). IEEE.
Blanchet, K., & James, P. (2011). How to do (or not to do) … a social network analysis in health systems research. Health Policy Plan, 2012 Aug; 27(5), 438446.
Bohannon, J. (2009). Counterterrorism’s new tool: ‘metanetwork’ analysis. Science, 325(5939), 409411.
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J.-S. S. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution, 24(3), 127135.
Bostandjiev, S., O’Donovan, J., Hall, C., Gretarsson, B., & Hollerer, T. (2011). WiGipedia: A tool for improving structured data in wikipedia. In Proceedings of the 2011 IEEE fifth international conference on semantic computing. ICSC ’11 (pp. 328335). Washington, DC, USA: IEEE Computer Society.
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s mechanical turk a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 35.
Burnham, K. P., & Anderson, D. R. (2003). Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media.
Chang, C., Bach, B., Dwyer, T., & Marriott, K. (2017). Evaluating perceptually complementary views for network exploration tasks. In Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 13971407). Denver, Colorado, USA, ACM.
Chang, R., Ziemkiewicz, C., Green, T. M., & Ribarsky, W. (2009). Defining insight for visual analytics. Computer Graphics and Applications, IEEE, 29(2), 1417.
Cohen, J., Cohen, P., & West, S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3 ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Eysenck, M. W., & Keane, M. T. (2013). Cognitive psychology: A student’s handbook. Psychology press.
Ghoniem, M., Fekete, J.-D., & Castagliola, P. (2004). A comparison of the readability of graphs using node-link and matrix-based representations. In Proceedings - IEEE symposium on information visualization (pp. 1724). Austin, TX, USA, IEEE.
Ghoniem, M., Fekete, J.-D., & Castagliola, P. (2005). On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Information Visualization, 4(2), 114135.
Gretarsson, B., O’Donovan, J., Bostandjiev, S., Höllerer, T., Asuncion, A., Newman, D., & Smyth, P. (2012). Topicnets: Visual analysis of large text corpora with topic modeling. ACM Transactions on Intelligent Systems and Technology, 3(2), 23:123:26.
Hall, D. L., Graham, J., & Catherman, E. (2015). A survey of tools and resources for the next generation analyst. In Proceedings Volume 9499, Next-Generation Analyst III; 94990, Event: SPIE Sensing Technology + Applications, 2015, Baltimore, Maryland, United States.
Hauser, D. J, & Schwarz, N. (2015). Attentive Tturkers: MTurk participants perform better on online attention checks than do subject pool participants. Behavior Research Methods, 48 (1), 400407.
Henry, N., Fekete, J.-D., & McGuffin, M. J. (2007). NodeTrix: a hybrid visualization of social networks. IEEE Transactions on Visualization and Computer Graphics, 13(6), 13021309.
Henry, N., & Fekete, J.-D. (2007). MatLink: Enhanced matrix visualization for analyzing social networks. In Proceedings Human-Computer Interaction – INTERACT 2007 (pp. 288302). Berlin, Heidelberg: Springer-Verlag.
Jaworowski, M., & Pavlak, S. (2003). Ali baba dataset ground truth. U.S. National Security Agency: Fort Meade, MD.
Kase, S. E., Roy, H., & Cassenti, D. N. (2015). Visualizing approaches for displaying measures of sentiment (Vol. 9499). In Proceedings Volume 9499, Next-Generation Analyst III; 94990H, Event: SPIE Sensing Technology + Applications, 2015, Baltimore, Maryland, United States.
Krebs, V. E. (2002). Mapping networks of terrorist cells. Connections, 24(3), 4352.
Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121.
Lankow, J., Ritchie, J., & Crooks, R. (2012). Infographics: The power of visual storytelling. Wiley.
Lee, B., Plaisant, C., Parr, C. S., Fekete, J.-D., & Henry, N. (2006). Task taxonomy for graph visualization. In Proceedings of the 2006 AVI workshop on BEyond time and errors, May 2006, Venezia, Italy (pp. 15). ACM.
MacCalman, M., MacCalman, A., & Wilson, G. (2013). Visualizing social networks to inform tactical engagement strategies that will influence the human domain. Small Wars Journal, 9(8).
McGrath, C., Blythe, J., & Krackhardt, D. (1997). The effect of spatial arrangement on judgments and errors in interpreting graphs. Social Networks, 19(3), 223242.
McIllwain, J. S. (1999). Organized crime: A social network approach. Crime, Law and Social Change, 32(4), 301323.
Mittrick, M., Roy, H., Kase, S., & Bowman, E. (2012) Refinement of the ali baba data set. US Army Research Laboratory, ARL-TN-0476.
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133142.
Newman, M. E. J. (2002). Spread of epidemic disease on networks. Physical Review E, 66(1), 016128.
North, C. (2006). Toward measuring visualization insight. Computer Graphics and Applications, IEEE, 26(3), 69.
Okoe, M., & Jianu, R. (2015). GraphUnit: Evaluating interactive graph visualizations using crowdsourcing. In Computer Graphics Forum (Vol. 34, pp. 451460). Wiley Online Library.
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716aac4716.
Peng, R. D. (2011). Reproducible research in computational science. Science, 334(6060), 12261227.
Purchase, H. C. (1998). Performance of layout algorithms: Comprehension, not computation. Journal of Visual Languages & Computing, 9(6), 647657.
Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403.
Schaffer, J., Giridhar, P., Jones, D., Höllerer, T., Abdelzaher, T., & O’Donovan, J. (2015). Getting the message?: A study of explanation interfaces for microblog data analysis. In Proceedings of the 20th international conference on intelligent user interfaces. Atlanta, Georgia, USA, ACM.
Sparrow, M. K. (1991). The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks, 13(3), 251274.
Sullivan, P. (1987). Newspaper graphics. Darmstadt, Germany: IFRA.
Von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J. J., Fekete, J.-D., & Fellner, D. W. (2011). Visual analysis of large graphs: state-of-the-art and future research challenges. In Computer Graphics Forum (Vol. 30, pp. 17191749). Wiley Online Library.
Wong, P. C., Foote, H., Mackey, P., Perrine, K., & Chin, G. Jr. (2006). Generating graphs for visual analytics through interactive sketching. IEEE Transactions on Visualization and Computer Graphics, 12(6), 13861398.
Yi, J. S., Kang, Y.-a., Stasko, J. T., & Jacko, J. A. (2008). Understanding and characterizing insights: how do people gain insights using information visualization? In Proceedings of the 2008 Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization (p. 4). Florence, Italy, ACM.

Keywords

Understanding node-link and matrix visualizations of networks: A large-scale online experiment

  • Donghao Ren (a1), Laura R. Marusich (a2), John O’Donovan (a3), Jonathan Z. Bakdash (a4), James A. Schaffer (a5), Daniel N. Cassenti (a6), Sue E. Kase (a7), Heather E. Roy (a8), Wan-yi (Sabrina) Lin (a9) and Tobias Höllerer (a10)...

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