Browsing by Author "Seo, Jinwook"

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    DSS: Drawing Dynamic Graphs with Spectral Sparsification
    (The Eurographics Association, 2022) Meidiana, Amyra; Hong, Seok-Hee; Pu, Yanyi; Lee, Justin; Eades, Peter; Seo, Jinwook; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
    This paper presents DSS (Dynamic Spectral Sparsification), a sampling approach for drawing large and complex dynamic graphs which can preserve important structural properties of the original graph. Specifically, we present two variants: DSSI (Independent) which performs spectral sparsification independently on each dynamic graph time slice; and DSS-U (Union) which performs spectral sparsification on the union graph of all time slices. Moreover, for evaluation of dynamic graph drawing using sampling approach, we introduce two new metrics: DSQ (Dynamic Sampling Quality) to measure how faithfully the samples represent the ground truth change in the dynamic graph, and DSDQ (Dynamic Sampling Drawing Quality) to measure how faithfully the drawings of the sample represent the ground truth change. Experiments demonstrate that DSS significantly outperform random sampling on quality metrics and visual comparison. On average, DSS obtains over 80% (resp., 30%) better DSQ (resp., DSDQ) than random sampling, and visually better preserves the ground truth changes in dynamic graphs.
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    Progressive Uniform Manifold Approximation and Projection
    (The Eurographics Association, 2020) Ko, Hyung-Kwon; Jo, Jaemin; Seo, Jinwook; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
    We present a progressive algorithm for the Uniform Manifold Approximation and Projection (UMAP), called the Progressive UMAP. Based on the theory of Riemannian geometry and algebraic topology, UMAP is an emerging dimensionality reduction technique that offers better versatility and stability than t-SNE. Although UMAP is also more efficient than t-SNE, it still suffers from an initial delay of a few minutes to produce the first projection, which limits its use in interactive data exploration. To tackle this problem, we improve the sequential computations in UMAP by making them progressive, which allows people to incrementally append a batch of data points into the projection at the desired pace. In our experiment with the Fashion MNIST dataset, we found that Progressive UMAP could generate the first approximate projection within a few seconds while also sufficiently capturing the important structures of the high-dimensional dataset.
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    Toward Understanding Representation Methods in Visualization Recommendations through Scatterplot Construction Tasks
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) L'Yi, Sehi; Chang, Youli; Shin, DongHwa; Seo, Jinwook; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Most visualization recommendation systems predominantly rely on graphical previews to describe alternative visual encodings. However, since InfoVis novices are not familiar with visual representations (e.g., interpretation barriers [GTS10]), novices might have difficulty understanding and choosing recommended visual encodings. As an initial step toward understanding effective representation methods for visualization recommendations, we investigate the effectiveness of three representation methods (i.e., previews, animated transitions, and textual descriptions) under scatterplot construction tasks. Our results show how different representations individually and cooperatively help users understand and choose recommended visualizations, for example, by supporting their expect-and-confirm process. Based on our study results, we discuss design implications for visualization recommendation interfaces.