3D Edge Bundling for Geographical Data Visualization

IEEE Trans. Visual. Comput. Graphics

Paulovich

et al. 2011

149

172

Abstract-In this paper, we present a novel approach for constructing bundled layouts of general graphs. As layout cues for bundles, we use medial axes, or skeletons, of edges which are similar in terms of position information. We combine edge clustering, distance fields, and 2D skeletonization to construct progressively bundled layouts for general graphs by iteratively attracting edges towards the centerlines of level sets of their distance fields. Apart from clustering, our entire pipeline is image-based with an efficient implementation in graphics hardware. Besides speed and implementation simplicity, our method allows explicit control of the emphasis on structure of the bundled layout, i.e. the creation of strongly branching (organic-like) or smooth bundles. We demonstrate our method on several large real-world graphs.Index Terms-Graph layouts, edge bundles, image-based information visualization.

Section: Related Workmentioning

confidence: 99%

Skeleton-Based Edge Bundling for Graph Visualization

Ersoy

IEEE Trans. Visual. Comput. Graphics

Paulovich

et al. 2011

149

172

“…Control meshes are used to route curved edges, e.g. [31,43], a Delaunay-based extension called geometric-based edge bundling (GBEB) [8], and 'winding roads' (WR) which use Voronoi diagrams for 2D and 3D layouts [25,24]. Skeleton-based edge bundling (SBEB) uses the skeleton of the graph drawing's thresholded distance transform as bundling cues to create strongly ramified bundles [12].…”

Section: Bundled Edge Graph Visualizationmentioning

confidence: 99%

Smooth bundling of large streaming and sequence graphs

2013 IEEE Pacific Visualization Symposium (PacificVis)

Ersoy

Telea

2013

Dynamic graphs are increasingly pervasive in modern information systems. However, understanding how a graph changes in time is difficult. We present here two techniques for simplified visualization of dynamic graphs using edge bundles. The first technique uses a recent image-based graph bundling method to create smoothly changing bundles from streaming graphs. The second technique incorporates additional edge-correspondence data and is thereby suited to visualize discrete graph sequences. We illustrate our methods with examples from real-world large dynamic graph datasets.

“…Gansner and Koren bundle edges in a circular node layout by area optimization metrics [14]. Edge routing can also use Delaunay [7] and Voronoi diagrams [29,28] reflecting the node positions. The most popular (and versatile) bundling methods use force-based techniques that use a force field designed to equal the gradient of a quality function to optimize [10,20], and have been adapted to separate bundles running in opposite directions [40].…”

Section: Introductionmentioning

confidence: 99%

Attribute-driven edge bundling for general graphs with applications in trail analysis

Peysakhovich

2015 IEEE Pacific Visualization Symposium (PacificVis)

Telea

2015

Edge bundling methods reduce visual clutter of dense and occluded graphs. However, existing bundling techniques either ignore edge properties such as direction and data attributes, or are otherwise computationally not scalable, which makes them unsuitable for tasks such as exploration of large trajectory datasets. We present a new framework to generate bundled graph layouts according to any numerical edge attributes such as directions, timestamps or weights. We propose a GPU-based implementation linear in number of edges, which makes our algorithm applicable to large datasets. We demonstrate our method with applications in the analysis of aircraft trajectory datasets and eye-movement traces.