• Corpus ID: 85510236

Number 3 Graph Bundling by Kernel Density Estimation

@inproceedings{Hurter2018Number3G,
  title={Number 3 Graph Bundling by Kernel Density Estimation},
  author={Christophe Hurter and Ozan Ersoy and Alexandru Cristian Telea},
  year={2018}
}
We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow. Our technique can be easily and efficiently implemented using standard graphics acceleration techniques and produces graph bundlings of similar appearance… 

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SHOWING 1-10 OF 33 REFERENCES
Skeleton-Based Edge Bundling for Graph Visualization
TLDR
A novel approach that combines 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.
Image‐Based Edge Bundles: Simplified Visualization of Large Graphs
We present a new approach aimed at understanding the structure of connections in edge‐bundling layouts. We combine the advantages of edge bundles with a bundle‐centric simplified visual
Multilevel agglomerative edge bundling for visualizing large graphs
TLDR
This work proposes a multilevel agglomerative edge bundling method based on a principled approach of minimizing ink needed to represent edges, with additional constraints on the curvature of the resulting splines.
Uncluttering Graph Layouts Using Anisotropic Diffusion and Mass Transport
TLDR
A physically inspired evolution process based on a modified heat equation is used to create an improved layout density image, making better use of available screen space and significantly accelerated using a graphics processing unit (GPU), resulting in the ability to handle large graphs in a matter of seconds.
3D Edge Bundling for Geographical Data Visualization
TLDR
This paper presents a generalization of [18] to reduce the clutter in a 3D representation by routing edges into bundles as well as a GPU-based rendering method to emphasize bundles densities while preserving edge color.
Winding Roads: Routing edges into bundles
TLDR
This paper introduces an intuitive edge bundling technique which efficiently reduces edge clutter in graphs drawings and a GPU‐based rendering method which helps users perceive bundles densities while preserving edge color.
Geometry-Based Edge Clustering for Graph Visualization
TLDR
A novel geometry-based edge-clustering framework that can group edges into bundles to reduce the overall edge crossings is proposed, which is intuitive, flexible, and efficient.
Flow map layout
TLDR
A method for generating flow maps using hierarchical clustering given a set of nodes, positions, and flow data between the nodes, inspired by graph layout algorithms that minimize edge crossings and distort node positions while maintaining their relative position to one another is presented.
Divided Edge Bundling for Directional Network Data
The node-link diagram is an intuitive and venerable way to depict a graph. To reduce clutter and improve the readability of node-link views, Holten & van Wijk's force-directed edge bundling employs a
Improved Circular Layouts
TLDR
Three independent, complementary techniques for lowering the density and improving the readability of circular layouts are suggested, able to reduce clutter, density and crossings compared with existing methods.
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