Evaluation of Graph Sampling: A Visualization Perspective

Wu, Yanhong; Cao, Nan; Archambault, Daniel; Shen, Qiaomu; Qu, Huamin; Cui, Wei

doi:10.1109/tvcg.2016.2598867

Cited by 58 publications

(28 citation statements)

References 49 publications

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“…Random Sampling Proxy graphs are representatives of larger graphs that are derived through sampling, filtering, or deriving a structural skeleton such as a spanning tree [ENH17, ZHA15, NHEM17, WCA*17, ZZC*17, RC05]. Therefore, these proxy graphs are missing some vertices and/or edges, and their visualizations will not show all the data.…”

Section: Related Workmentioning

confidence: 99%

A Random Sampling O(n) Force‐calculation Algorithm for Graph Layouts

Gove¹

2019

Computer Graphics Forum

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This paper proposes a linear‐time repulsive‐force‐calculation algorithm with sub‐linear auxiliary space requirements, achieving an asymptotic improvement over the Barnes‐Hut and Fast Multipole Method force‐calculation algorithms. The algorithm, named random vertex sampling (RVS), achieves its speed by updating a random sample of vertices at each iteration, each with a random sample of repulsive forces. This paper also proposes a combination algorithm that uses RVS to derive an initial layout and then applies Barnes‐Hut to refine the layout. An evaluation of RVS and the combination algorithm compares their speed and quality on 109 graphs against a Barnes‐Hut layout algorithm. The RVS algorithm performs up to 6.1 times faster on the tested graphs while maintaining comparable layout quality. The combination algorithm also performs faster than Barnes‐Hut, but produces layouts that are more symmetric than using RVS alone. Data and code: https://osf.io/nb7m8/

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Section: Related Workmentioning

confidence: 99%

A Random Sampling O(n) Force‐calculation Algorithm for Graph Layouts

Gove¹

2019

Computer Graphics Forum

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“…Among the studies that are included in our survey, 15 studies use graphs with more than 1,000 nodes [12,24,37,83,88,89,95,124,132,136,138,143,152] and another nine that use graphs with more than 500 nodes [75,84,94,99,109,120,143,146].…”

Section: A Number Of Nodesmentioning

confidence: 99%

Exploring the limits of complexity: A survey of empirical studies on graph visualisation

et al. 2018

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For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks. In both bodies of literature, networks are frequently referred to as being 'large' or 'complex', yet these terms are relative. From a human-centred, experiment point-of-view, what constitutes 'large' (for example) depends on several factors, such as data complexity, visual complexity, and the technology used. In this paper, we survey the literature on humancentred experiments to understand how, in practice, different features and characteristics of node-link diagrams affect visual complexity.

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“…For example, the algorithms tend to select the nodes with common degrees far more than the nodes with rare degrees to maintain the power law of degree distribution [66]. Human viewers prefer to observe large structures in advance but may ignore small structures when judging whether a sample is visually similar to the original graph [43,75].…”

Section: Introductionmentioning

confidence: 99%

Preserving Minority Structures in Graph Sampling

Zhao

Jiang

Qian

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph sampling, we assume that existing algorithms cannot effectively preserve minority structures that are rare and small in a graph but are very important in graph analysis. In this work, we initially conduct a pilot user study to investigate representative minority structures that are most appealing to human viewers. We then perform an experimental study to evaluate the performance of existing graph sampling algorithms regarding minority structure preservation. Results confirm our assumption and suggest key points for designing a new graph sampling approach named mino-centric graph sampling (MCGS). In this approach, a triangle-based algorithm and a cut-point-based algorithm are proposed to efficiently identify minority structures. A set of importance assessment criteria are designed to guide the preservation of important minority structures. Three optimization objectives are introduced into a greedy strategy to balance the preservation between minority and majority structures and suppress the generation of new minority structures. A series of experiments and case studies are conducted to evaluate the effectiveness of the proposed MCGS.

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Evaluation of Graph Sampling: A Visualization Perspective

Cited by 58 publications

References 49 publications

A Random Sampling O(n) Force‐calculation Algorithm for Graph Layouts

A Random Sampling O(n) Force‐calculation Algorithm for Graph Layouts

Exploring the limits of complexity: A survey of empirical studies on graph visualisation

Preserving Minority Structures in Graph Sampling

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