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1. Evaluating Multivariate Network Visualization Techniques Using a Validated Design and Crowdsourcing Approach

   https://doi.org/10.1145/3313831.3376381  

   Nobre, Carolina; Wootton, Dylan; Harrison, Lane; Lex, Alexander (April 2020, CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   null (Ed.)

   Visualizing multivariate networks is challenging because of the trade-offs necessary for effectively encoding network topology and encoding the attributes associated with nodes and edges. A large number of multivariate network visualization techniques exist, yet there is little empirical guidance on their respective strengths and weaknesses. In this paper, we describe a crowdsourced experiment, comparing node-link diagrams with on-node encoding and adjacency matrices with juxtaposed tables. We find that node-link diagrams are best suited for tasks that require close integration between the network topology and a few attributes. Adjacency matrices perform well for tasks related to clusters and when many attributes need to be considered. We also reflect on our method of using validated designs for empirically evaluating complex, interactive visualizations in a crowdsourced setting. We highlight the importance of training, compensation, and provenance tracking. 

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2. Modeling and Measuring the Chart Communication Recall Process

   https://doi.org/10.1111/cgf.70099  

   Arunkumar, A; Padilla, L; Bryan, C (May 2025, Computer Graphics Forum)

   Understanding memory in the context of data visualizations is paramount for effective design. While immediate clarity in a visualization is crucial, retention of its information determines its long‐term impact. While extensive research has underscored the elements enhancing visualization memorability, a limited body of work has delved into modeling the recall process. This study investigates the temporal dynamics of visualization recall, focusing on factors influencing recollection, shifts in recall veracity, and the role of participant demographics. Using data from an empirical study (n = 104), we propose a novel approach combining temporal clustering and handcrafted features to model recall over time. A long short‐term memory (LSTM) model with attention mechanisms predicts recall patterns, revealing alignment with informativeness scores and participant characteristics. Our findings show that perceived informativeness dictates recall focus, with more informative visualizations eliciting narrative‐driven insights and less informative ones prompting aesthetic‐driven responses. Recall accuracy diminishes over time, particularly for unfamiliar visualizations, with age and education significantly shaping recall emphases. These insights advance our understanding of visualization recall, offering practical guidance for designing visualizations that enhance retention and comprehension. All data and materials are available at: https://osf.io/ghe2j/'>https://osf.io/ghe2j/. 

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3. reVISit: Looking Under the Hood of Interactive Visualization Studies

   https://doi.org/10.1145/3411764.3445382  

   Nobre, Carolina; Wootton, Dylan; Cutler, Zach; Harrison, Lane; Pfister, Hanspeter; Lex, Alexander (May 2021, SIGCHI Conference on Human Factors in Computing Systems (CHI))

   Quantifying user performance with metrics such as time and accuracy does not show the whole picture when researchers evaluate complex, interactive visualization tools. In such systems, performance is often influenced by different analysis strategies that statistical analysis methods cannot account for. To remedy this lack of nuance, we propose a novel analysis methodology for evaluating complex interactive visualizations at scale. We implement our analysis methods in reVISit, which enables analysts to explore participant interaction performance metrics and responses in the context of users' analysis strategies. Replays of participant sessions can aid in identifying usability problems during pilot studies and make individual analysis processes salient. To demonstrate the applicability of reVISit to visualization studies, we analyze participant data from two published crowdsourced studies. Our findings show that reVISit can be used to reveal and describe novel interaction patterns, to analyze performance differences between different analysis strategies, and to validate or challenge design decisions. 

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4. Multi-level tree based approach for interactive graph visualization with semantic zoom

   De Luca, F.; Hossain, I.; Kobourov, S.; Börner, K. (January 2019, ArXiv.org)

   A recent data visualization literacy study shows that most people cannot read networks that use hierarchical cluster representations such as “supernoding” and “edge bundling.” Other studies that compare standard node-link representations with map-like visualizations show that map-like visualizations are superior in terms of task performance, memorization and engagement. With this in mind, we propose the Zoomable Multi-Level Tree (ZMLT) algorithm for maplike visualization of large graphs that is representative, real, persistent, overlapfree labeled, planar, and compact. These six desirable properties are formalized with the following guarantees: (1) The abstract and embedded trees represent the underlying graph appropriately at different level of details (in terms of the structure of the graph as well as the embedding thereof); (2) At every level of detail we show real vertices and real paths from the underlying graph; (3) If any node or edge appears in a given level, then they also appear in all deeper levels; (4) All nodes at the current level and higher levels are labeled and there are no label overlaps; (5) There are no crossings on any level; (6) The drawing area is proportional to the total area of the labels. This algorithm is implemented and we have a functional prototype for the interactive interface in a web browser. 

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5. Drawing Graphs on the Sphere

   https://doi.org/10.1145/3399715.3399915  

   Perry, S; Gray, K; Yin, M; Kobourov, S (October 2020, 13th International Conference on Advanced Visual Interfaces (AVI))

   Graphs are most often visualized in the two dimensional Euclidean plane, but spherical space offers several advantages when visualizing graphs. First, some graphs such as skeletons of three dimensional polytopes (tetrahedron, cube, icosahedron) have spherical realizations that capture their 3D structure, which cannot be visualized as well in the Euclidean plane. Second, the sphere makes possible a natural “focus + context" visualization with more detail in the center of the view and less details away from the center. Finally, whereas layouts in the Euclidean plane implicitly define notions of “central" and “peripheral" nodes, this issue is reduced on the sphere, where the layout can be centered at any node of interest. We first consider a projection-reprojection method that relies on transformations often seen in cartography and describe the implementation of this method in the GMap visualization system. This approach allows many different types of 2D graph visualizations, such as node-link diagrams, LineSets, BubbleSets and MapSets, to be converted into spherical web browser visualizations. Next we consider an approach based on spherical multidimensional scaling, which performs graph layout directly on the sphere. This approach supports node-link diagrams and GMap-style visualizations, rendered in the web browser using WebGL. 

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