Node Overlap Removal Algorithms: an Extended Comparative Study

Chen, Fati; Piccinini, Laurent; Poncelet, Pascal; Sallaberry, Arnaud

doi:10.7155/jgaa.00532

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…A review of the Nodes Dispersion literature was presented in Chen et al's survey [3,4]. PFS [29], PFS' [14], FTA [16], RWordle-L [33] are orthogonal iterative algorithms that rely on scan-line [7] to identify overlaps in a layout and process them.…”

Section: Geometric Space Algorithms (Nodes Dispersion)mentioning

confidence: 99%

“…The quality metrics considered in this evaluation are taken or inspired from Li et al [24] and Chen et al [3,4]. We made adjustments to some selected metrics to better reflect their purpose.…”

Section: Evaluation Protocol 411 Metricsmentioning

confidence: 99%

“…Normalized node movements This metric inspired from Chen et al [3,4] quantifies the relative node movements made from the original layout to the adjusted one. Before computing movement, a scale and shift operation superimposes the adjusted layout on the original one and not the opposite as proposed in [3,4]). It enables the movement quantity to be unsensitive to offsets and movements induced by upscaling effects.…”

Section: Evaluation Protocol 411 Metricsmentioning

confidence: 99%

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Guaranteed Visibility in Scatterplots with Tolerance

Giovannangeli,

Lalanne,

Giot

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Geometric Space Algorithms (Nodes Dispersion)mentioning

confidence: 99%

Section: Evaluation Protocol 411 Metricsmentioning

confidence: 99%

Section: Evaluation Protocol 411 Metricsmentioning

confidence: 99%

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Guaranteed Visibility in Scatterplots with Tolerance

Giovannangeli,

Lalanne,

Giot

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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“…Relative Positioning Preservation (RPP) is computed using the Orthogonal Ordering metric primarily designed to evaluate shape preservation of overlap solvers in graph drawing [22]. We use this metric to evaluate how well the position of each element relative to others is preserved before and after computing the grid arrangement.…”

Section: A Evaluation Metricsmentioning

confidence: 99%

“…Global Positioning Preservation (GPP) is being computed using the Node Movements metric. It evaluates how coordinates moved from the original projection to the resulting arrangement [22]. While coordinate movements are unavoidable if the shape of the projection is altered, we assume the faithful grid arrangement of 2D data involves minimal movements.…”

Section: A Evaluation Metricsmentioning

confidence: 99%

VRGrid: Efficient Transformation of 2D Data into Pixel Grid Layout

Halnaut

Giot

Bourqui

2022

2022 26th International Conference Information Visualisation (IV)

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Projecting a set of n points on a grid of size √ n× √ n provides the best possible information density in two dimensions without overlap. We leverage the Voronoi Relaxation method to devise a novel and versatile post-processing algorithm called VRGrid: it enables the arrangement of any 2D data on a grid while preserving its initial positions. We apply VRGrid to generate compact and overlap-free visualization of popular and overlap-prone projection methods (e.g., t-SNE). We prove that our method complexity is O ( √ n.i.n.log(n)), with i a determined maximum number of iterations and n the input dataset size. It is thus usable for visualization of several thousands of points. We evaluate VRGrid's efficiency with several metrics: distance preservation (DP), neighborhood preservation (NP), pairwise relative positioning preservation (RPP) and global positioning preservation (GPP). We benchmark VRGrid against two stateof-the-art methods: Self-Sorting Maps (SSM) and Distancepreserving Grid (DGrid). VRGrid outperforms these two methods, given enough iterations, on DP, RPP and GPP which we identify to be the key metrics to preserve the positions of the original set of points.

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