Sparse stress minimization: Difference between revisions

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=Sparse Stress Layout=
=Sparse Stress Layout=
===Method===
===Method===
The Sparse Stress Layout is a heuristic to approximate the (full) stress layout. The goal of the stress minimization is to find a layout, <math>x</math>, such that <math>\sum_{i<j}w_{ij}(||x_i - x_j|| - d_{ij})^2 \text{ is as small as possible.}</math> In other words stress tries to find a layout in which the euclidean distance of each pair of nodes matches their graph-theoretical distance, i.e. shortest-path distance.  
The sparse stress layout is a heuristic to approximate the (full) stress layout. The goal of the stress minimization is to find a layout, <math>x</math>, such that <math>\sum_{i<j}w_{ij}(||x_i - x_j|| - d_{ij})^2 \text{ is as small as possible.}</math> In other words stress minimization tries to find a layout in which the euclidean distance of each pair of nodes matches their graph-theoretical distance, i.e. shortest-path distance.  
 
In comparison to the (full) stress minimization, sparse stress on the one hand requires less space and running time, but on the other hand the resulting layouts have a slightly lower quality. The space and running time reduction is achieved by restricting the stress function from <math>n^2</math> to <math>m+np</math> with <math>p</math> denoting the number of pivots. 
 
More detailed background information can be found in
* Mark Ortmann, Mirza Klimenta, and Ulrik Brandes: [https://dx.doi.org/10.1007/978-3-319-50106-2_2 A sparse Stress Model], GD'16, 18-32, 2016.
 
=== Complexity ===
=== Complexity ===

Revision as of 17:10, 23 February 2017

Sparse Stress Layout

Method

The sparse stress layout is a heuristic to approximate the (full) stress layout. The goal of the stress minimization is to find a layout, x, such that i<jwij(||xixj||dij)2 is as small as possible. In other words stress minimization tries to find a layout in which the euclidean distance of each pair of nodes matches their graph-theoretical distance, i.e. shortest-path distance.

In comparison to the (full) stress minimization, sparse stress on the one hand requires less space and running time, but on the other hand the resulting layouts have a slightly lower quality. The space and running time reduction is achieved by restricting the stress function from n2 to m+np with p denoting the number of pivots.

More detailed background information can be found in

Complexity