Structure-aware Fisheye Views for Efficient Large Graph Exploration

Wang, Yunhai; Wang, Yanyan; Zhang, Haifeng; Sun, Yinqi; Fu, Chi‐Wing; Sedlmair, Michael; Chen, Baoquan; Deußen, Oliver

doi:10.1109/tvcg.2018.2864911

Cited by 26 publications

(19 citation statements)

References 46 publications

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“…Another family of interactive techniques for exploring graphs involve applying spatial distortion around regions of interest to achieve a kind of "structure aware zooming", e.g. [58]. It would be interesting to compare the efficacy of these techniques against interactive sphere and torus projections.…”

Section: Discussionmentioning

confidence: 99%

GAN'SDA Wrap: Geographic And Network Structured Data on surfaces that Wrap around

Chen,

Dwyer,

Yang

et al. 2022

Preprint

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network layouts to 2D screens has not been studied, nor have such spherical network projections been compared to torus projections. Using the most successful interactive sphere projections from our first study, we compare spherical, standard and toroidal layouts of networks for cluster and path following tasks with 96 participants, finding benefits for both spherical and toroidal layouts over standard network layouts in terms of accuracy for cluster understanding tasks. CCS CONCEPTS• Human-centered computing → Graph drawings; Empirical studies in visualization; Geographic visualization.

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Section: Discussionmentioning

confidence: 99%

GAN'SDA Wrap: Geographic And Network Structured Data on surfaces that Wrap around

Chen,

Dwyer,

Yang

et al. 2022

Preprint

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“…Wang et al [53] propose a revised formulation of stress that can be used to specify ideal edge direction in addition to ideal edge lengths in a graph drawing. Wang et al [54] extended that stress formulation to produce structure-aware and smooth fish-eye views of graphs. Devkota et al [18] also use a stress-based approach to minimize edge crossings and maximize crossing angles.…”

Section: Related Workmentioning

confidence: 99%

Multicriteria Scalable Graph Drawing via Stochastic Gradient Descent, $(SGD)^2$

Ahmed¹,

Luca²,

Devkota³

et al. 2021

Preprint

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Readability criteria, such as distance or neighborhood preservation, are often used to optimize node-link representations of graphs to enable the comprehension of the underlying data. With few exceptions, graph drawing algorithms typically optimize one such criterion, usually at the expense of others. We propose a layout approach, Multicriteria Scalable Graph Drawing via Stochastic Gradient Descent, (SGD) 2 , that can handle multiple readability criteria. (SGD) 2 can optimize any criterion that can be described by a differentiable function. Our approach is flexible and can be used to optimize several criteria that have already been considered earlier (e.g., obtaining ideal edge lengths, stress, neighborhood preservation) as well as other criteria which have not yet been explicitly optimized in such fashion (e.g., node resolution, angular resolution, aspect ratio). The approach is scalable and can handle large graphs. A variation of the underlying approach can also be used to optimize many desirable properties in planar graphs, while maintaining planarity. Finally, we provide quantitative and qualitative evidence of the effectiveness of (SGD) 2 : we analyze the interactions between criteria, measure the quality of layouts generated from (SGD) 2 as well as the runtime behavior, and analyze the impact of sample sizes. The source code is available on github and we also provide an interactive demo for small graphs.

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“…For example, SchemeLens [8] reveals orthogonally laid-out diagrams. And the structure-aware fisheye proposed in [62] reduces spatial and temporal distortions. Compared to these solutions, our method supports the user's defined input to customize layout.…”

Section: Interaction Techniquesmentioning

confidence: 99%