layout_pmds: pivot MDS graph layout

In schochastics/smglr: Additional Layout Algorithms for Network Visualizations

pivot MDS graph layout

Description

similar to layout_with_mds but uses only a small set of pivots for MDS. Considerably faster than MDS and thus applicable for larger graphs.

Usage

layout_with_pmds(g, pivots, weights = NA, D = NULL, dim = 2)

layout_igraph_pmds(g, pivots, weights = NA, D = NULL, circular)

Arguments

g	igraph object
pivots	number of pivots
weights	possibly a numeric vector with edge weights. If this is NULL and the graph has a weight edge attribute, then the attribute is used. If this is NA then no weights are used (even if the graph has a weight attribute). By default, weights are ignored. See details for more.
D	precomputed distances from pivots to all nodes (if available, default: NULL)
dim	dimensionality of layout (defaults to 2)
circular	not used

Details

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight)

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Value

matrix of coordinates

Author(s)

David Schoch

References

Brandes, U. and Pich, C. (2006). Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. In International Symposium on Graph Drawing (pp. 42-53). Springer

Examples

## Not run: 
library(igraph)
library(ggraph)

g <- sample_gnp(1000, 0.01)

xy <- layout_with_pmds(g, pivots = 100)

## End(Not run)

schochastics/smglr documentation built on March 13, 2024, 4:14 a.m.