Algorithms for Cluster Busting in Anchored Graph Drawing

Meijer, Henk; Rappaport, David

doi:10.1142/9789812777638_0004

Cited by 28 publications

(40 citation statements)

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“…However, the most popular approach in recent years is to compute time varying network layouts by adding additional constraints that anchor vertices to their positions in the previous timestep [67,42,43]. These techniques work by adding some additional forces to the force direction calculation, but provide a good balance of cost, layout quality, and stability, and can be tuned by adjusting the anchor weights.…”

Section: Dynamic Graph Layoutsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Multivariate Network Visualization

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Abstract. Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, its the structure of the dynamic graph that evolves as vertices and edges are added or removed from the graph. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also explore future applications and directions for this emerging area in the fields of information visualization and graph drawing.

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Section: Dynamic Graph Layoutsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Multivariate Network Visualization

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“…The graphs are selected randomly with the criteria that a graph chosen should be connected, and is of relatively large size. We compared PRISM with an implementation in Graphviz of the solve VPSC algorithm [3] 2 , hereafter denoted as VPSC, as well as VORO, the Voronoi cluster busting algorithm [10,22]. The final algorithm is the ODNLS algorithm of Li et al [21], which relies on varied edge lengths in a spring embedder.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…A number of such algorithms have been proposed. For example, the Voronoi cluster busting algorithm [10,22] works by iteratively forming a Voronoi diagram from the current layout and moving each node to the center of its Voronoi cell until no overlaps remain. Although roughly maintaining relative node positions, the overall affect is to lose much of the layout structure.…”

Section: Introductionmentioning

confidence: 99%

Efficient Node Overlap Removal Using a Proximity Stress Model

Gansner

2009

Graph Drawing

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Abstract. When drawing graphs whose nodes contain text or graphics, the nontrivial node sizes must be taken into account, either as part of the initial layout or as a post-processing step. The core problem is to avoid overlaps while retaining the structural information inherent in a layout using little additional area. This paper presents a new node overlap removal algorithm that does well by these measures.

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“…The uniformity of the cells of an optimal CVT has been conjectured by Gersho [3] and proved in 2D [4], while confirmed empirically in 3D [5]. This property makes the CVT useful in many applications, including graph drawing [6], decorative arts simulation [7], [8], [9], grid generation and optimization [10], vector field visualization [11], [12], surface remeshing [13], [14], [15], [16] and medial axis approximation [17]. In this paper we study how to speed up the computation of the CVT using the GPU.…”

Section: Introductionmentioning

confidence: 85%

GPU-Assisted Computation of Centroidal Voronoi Tessellation

Rong

Liu

Wang

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Abstract-Centroidal Voronoi tessellations (CVT) are widely used in computational science and engineering. The most commonly used method is Lloyd's method, and recently the L-BFGS method is shown to be faster than Lloyd's method for computing the CVT. However, these methods run on the CPU and are still too slow for many practical applications. We present techniques to implement these methods on the GPU for computing the CVT on 2D planes and on surfaces, and demonstrate significant speedup of these GPU-based methods over their CPU counterparts. For CVT computation on a surface, we use a geometry image stored in the GPU to represent the surface for computing the Voronoi diagram on the surface. In our implementation a new technique is proposed for parallel regional reduction on the GPU for evaluating integrals over Voronoi cells.

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Algorithms for Cluster Busting in Anchored Graph Drawing

Cited by 28 publications

References 0 publications

Temporal Multivariate Networks

Temporal Multivariate Networks

Efficient Node Overlap Removal Using a Proximity Stress Model

GPU-Assisted Computation of Centroidal Voronoi Tessellation

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