Browsing by Author "Meidiana, Amyra"

Now showing 1 - 4 of 4
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    DNC: Dynamic Neighborhood Change Faithfulness Metrics
    (The Eurographics Association, 2022) Cai, Shijun; Meidiana, Amyra; Hong, Seok-Hee; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
    Faithfulness metrics measure how faithfully a visualization displays the ground truth information of the data. For example, neighborhood faithfulness metrics measure how faithfully the geometric neighbors of vertices in a graph drawing represent the ground truth neighbors of vertices in the graph. This paper presents a new dynamic neighborhood change (DNC) faithfulness metric for dynamic graphs to measure how proportional the geometric neighborhood change in dynamic graph drawings is to the ground truth neighborhood change in dynamic graphs. We validate the DNC metrics using deformation experiments, demonstrating that it can accurately measure neighborhood change faithfulness in dynamic graph drawings. We then present extensive comparison experiments to evaluate popular graph drawing algorithms using DNC, to recommend which layout obtains the highest neighborhood change faithfulness on a variety of dynamic graphs.
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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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    GDot-i: Interactive System for Dot Paintings of Graphs
    (The Eurographics Association, 2022) Eades, Peter; Hong, Seok-Hee; McGrane, Martin; Meidiana, Amyra; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    This poster presents GDot-i, an interactive system visualizing graphs and networks as dot paintings, inspired by the dot painting style of Central Australia. We describe the implementation of GDot-i, a web-based interactive system, including the user interface and typical use cases.
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    NFGD: Neighborhood-Faithful Graph Drawing
    (The Eurographics Association, 2025) Fan, Yuming; Hong, Seok-Hee; Meidiana, Amyra; El-Assady, Mennatallah; Ottley, Alvitta; Tominski, Christian
    Neighborhood faithfulness metrics measure how faithfully the ground truth neighbors of vertices in a graph G are represented as the geometric neighbors of vertices in a drawing D of G. In this paper, we present NFGD, a post-processing algorithm for optimizing the neighborhood faithfulness of graph drawings. Experiments demonstrate the effectiveness of NFGD for computing neighbor-faithful drawings, on average 320% improvement over the popular graph drawing algorithms: 425% over Stress Majorization (SM) and 215% over force-directed algorithm Fruchterman-Reingold (FR). In particular, for scale-free graphs, NFGD-SM achieves 776% improvement over SM and NFGD-FR obtains 597% improvement over FR.