[](https://github.com/schochastics/graphlayouts/actions)
[](https://cran.r-project.org/package=graphlayouts)
[](https://CRAN.R-project.org/package=graphlayouts)
[](https://CRAN.R-project.org/package=graphlayouts)
[](https://app.codecov.io/gh/schochastics/graphlayouts?branch=main)
[](https://doi.org/10.5281/zenodo.7870213)
[](https://doi.org/10.21105/joss.05238)
This package implements some graph layout algorithms that are not
available in `igraph`.
**A detailed introductory tutorial for graphlayouts and ggraph can be
found [here](https://schochastics.github.io/netVizR/).**
The package implements the following algorithms:
- Stress majorization
([Paper](https://graphviz.gitlab.io/_pages/Documentation/GKN04.pdf))
- Quadrilateral backbone layout
([Paper](https://doi.org/10.7155/jgaa.00370))
- flexible radial layouts
([Paper](https://www.uni-konstanz.de/algo/publications/bp-mfrl-11.pdf))
- sparse stress ([Paper](https://arxiv.org/abs/1608.08909))
- pivot MDS ([Paper](https://doi.org/10.1007/978-3-540-70904-6_6))
- dynamic layout for longitudinal data
([Paper](https://doi.org/10.1016/j.socnet.2011.06.002))
- spectral layouts (adjacency/Laplacian)
- a simple multilevel layout
- a layout algorithm using UMAP
- group based centrality and focus layouts which keeps groups of nodes
close in the same range on the concentric circle
## Install
``` r
# dev version
remotes::install_github("schochastics/graphlayouts")
# CRAN
install.packages("graphlayouts")
```
## Stress Majorization: Connected Network
*This example is a bit of a special case since it exploits some weird
issues in igraph.*
``` r
library(igraph)
library(ggraph)
library(graphlayouts)
set.seed(666)
pa <- sample_pa(1000, 1, 1, directed = F)
ggraph(pa, layout = "nicely") +
geom_edge_link0(width = 0.2, colour = "grey") +
geom_node_point(col = "black", size = 0.3) +
theme_graph()
```
``` r
ggraph(pa, layout = "stress") +
geom_edge_link0(width = 0.2, colour = "grey") +
geom_node_point(col = "black", size = 0.3) +
theme_graph()
```
## Stress Majorization: Unconnected Network
Stress majorization also works for networks with several components. It
relies on a bin packing algorithm to efficiently put the components in a
rectangle, rather than a circle.
``` r
set.seed(666)
g <- disjoint_union(
sample_pa(10, directed = FALSE),
sample_pa(20, directed = FALSE),
sample_pa(30, directed = FALSE),
sample_pa(40, directed = FALSE),
sample_pa(50, directed = FALSE),
sample_pa(60, directed = FALSE),
sample_pa(80, directed = FALSE)
)
ggraph(g, layout = "nicely") +
geom_edge_link0() +
geom_node_point() +
theme_graph()
```
``` r
ggraph(g, layout = "stress", bbox = 40) +
geom_edge_link0() +
geom_node_point() +
theme_graph()
```
## Backbone Layout
Backbone layouts are helpful for drawing hairballs.
``` r
set.seed(665)
# create network with a group structure
g <- sample_islands(9, 40, 0.4, 15)
g <- simplify(g)
V(g)$grp <- as.character(rep(1:9, each = 40))
ggraph(g, layout = "stress") +
geom_edge_link0(colour = rgb(0, 0, 0, 0.5), width = 0.1) +
geom_node_point(aes(col = grp)) +
scale_color_brewer(palette = "Set1") +
theme_graph() +
theme(legend.position = "none")
```
The backbone layout helps to uncover potential group structures based on
edge embeddedness and puts more emphasis on this structure in the
layout.
``` r
bb <- layout_as_backbone(g, keep = 0.4)
E(g)$col <- FALSE
E(g)$col[bb$backbone] <- TRUE
ggraph(g, layout = "manual", x = bb$xy[, 1], y = bb$xy[, 2]) +
geom_edge_link0(aes(col = col), width = 0.1) +
geom_node_point(aes(col = grp)) +
scale_color_brewer(palette = "Set1") +
scale_edge_color_manual(values = c(rgb(0, 0, 0, 0.3), rgb(0, 0, 0, 1))) +
theme_graph() +
theme(legend.position = "none")
```
## Radial Layout with Focal Node
The function `layout_with_focus()` creates a radial layout around a
focal node. All nodes with the same distance from the focal node are on
the same circle.
``` r
library(igraphdata)
library(patchwork)
data("karate")
p1 <- ggraph(karate, layout = "focus", focus = 1) +
draw_circle(use = "focus", max.circle = 3) +
geom_edge_link0(edge_color = "black", edge_width = 0.3) +
geom_node_point(aes(fill = as.factor(Faction)), size = 2, shape = 21) +
scale_fill_manual(values = c("#8B2323", "#EEAD0E")) +
theme_graph() +
theme(legend.position = "none") +
coord_fixed() +
labs(title = "Focus on Mr. Hi")
p2 <- ggraph(karate, layout = "focus", focus = 34) +
draw_circle(use = "focus", max.circle = 4) +
geom_edge_link0(edge_color = "black", edge_width = 0.3) +
geom_node_point(aes(fill = as.factor(Faction)), size = 2, shape = 21) +
scale_fill_manual(values = c("#8B2323", "#EEAD0E")) +
theme_graph() +
theme(legend.position = "none") +
coord_fixed() +
labs(title = "Focus on John A.")
p1 + p2
```
## Radial Centrality Layout
The function `layout_with_centrality` creates a radial layout around the
node with the highest centrality value. The further outside a node is,
the more peripheral it is.
``` r
library(igraphdata)
library(patchwork)
data("karate")
bc <- betweenness(karate)
p1 <- ggraph(karate, layout = "centrality", centrality = bc, tseq = seq(0, 1, 0.15)) +
draw_circle(use = "cent") +
annotate_circle(bc, format = "", pos = "bottom") +
geom_edge_link0(edge_color = "black", edge_width = 0.3) +
geom_node_point(aes(fill = as.factor(Faction)), size = 2, shape = 21) +
scale_fill_manual(values = c("#8B2323", "#EEAD0E")) +
theme_graph() +
theme(legend.position = "none") +
coord_fixed() +
labs(title = "betweenness centrality")
cc <- closeness(karate)
p2 <- ggraph(karate, layout = "centrality", centrality = cc, tseq = seq(0, 1, 0.2)) +
draw_circle(use = "cent") +
annotate_circle(cc, format = "scientific", pos = "bottom") +
geom_edge_link0(edge_color = "black", edge_width = 0.3) +
geom_node_point(aes(fill = as.factor(Faction)), size = 2, shape = 21) +
scale_fill_manual(values = c("#8B2323", "#EEAD0E")) +
theme_graph() +
theme(legend.position = "none") +
coord_fixed() +
labs(title = "closeness centrality")
p1 + p2
```
## Large graphs
`graphlayouts` implements two algorithms for visualizing large networks
(\<100k nodes). `layout_with_pmds()` is similar to `layout_with_mds()`
but performs the multidimensional scaling only with a small number of
pivot nodes. Usually, 50-100 are enough to obtain similar results to the
full MDS.
`layout_with_sparse_stress()` performs stress majorization only with a
small number of pivots (~50-100). The runtime performance is inferior to
pivotMDS but the quality is far superior.
A comparison of runtimes and layout quality can be found in the
[wiki](https://github.com/schochastics/graphlayouts/wiki/)
**tl;dr**: both layout algorithms appear to be faster than the fastest
igraph algorithm `layout_with_drl()`.
Below are two examples of layouts generated for large graphs using
`layout_with_sparse_stress()`
A retweet network with 18k nodes and 61k edges
A network of football players with 165K nodes and 6M edges.
## dynamic layouts
`layout_as_dynamic()` allows you to visualize snapshots of longitudinal
network data. Nodes are anchored with a reference layout and only moved
slightly in each wave depending on deleted/added edges. In this way, it
is easy to track down specific nodes throughout time. Use `patchwork` to
put the individual plots next to each other.
``` r
# remotes::install_github("schochastics/networkdata")
library(networkdata)
# longitudinal dataset of friendships in a school class
data("s50")
xy <- layout_as_dynamic(s50, alpha = 0.2)
pList <- vector("list", length(s50))
for (i in seq_along(s50)) {
pList[[i]] <- ggraph(s50[[i]], layout = "manual", x = xy[[i]][, 1], y = xy[[i]][, 2]) +
geom_edge_link0(edge_width = 0.6, edge_colour = "grey66") +
geom_node_point(shape = 21, aes(fill = as.factor(smoke)), size = 3) +
geom_node_text(aes(label = 1:50), repel = T) +
scale_fill_manual(
values = c("forestgreen", "grey25", "firebrick"),
labels = c("no", "occasional", "regular"),
name = "smoking",
guide = ifelse(i != 2, "none", "legend")
) +
theme_graph() +
theme(legend.position = "bottom") +
labs(title = paste0("Wave ", i))
}
wrap_plots(pList)
```
## Layout manipulation
The functions `layout_mirror()` and `layout_rotate()` can be used to
manipulate an existing layout
# How to reach out?
### Where do I report bugs?
Simply [open an
issue](https://github.com/schochastics/graphlayouts/issues/new) on
GitHub.
### How do I contribute to the package?
If you have an idea (but no code yet), [open an
issue](https://github.com/schochastics/graphlayouts/issues/new) on
GitHub. If you want to contribute with a specific feature and have the
code ready, fork the repository, add your code, and create a pull
request.
### Do you need support?
The easiest way is to [open an
issue](https://github.com/schochastics/graphlayouts/issues/new) - this
way, your question is also visible to others who may face similar
problems.
### Code of Conduct
Please note that the graphlayouts project is released with a
[Contributor Code of
Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.
graphlayouts/man/ 0000755 0001762 0000144 00000000000 14744174674 013571 5 ustar ligges users graphlayouts/man/graphlayouts-package.Rd 0000644 0001762 0000144 00000003310 14744174674 020170 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/graphlayouts.R
\docType{package}
\name{graphlayouts-package}
\alias{graphlayouts}
\alias{graphlayouts-package}
\title{graphlayouts: layout algorithms for network visualizations}
\description{
The package implements several new layout algorithms to visualize networks.
Most are based on the concept of stress majorization. Some more specific algorithms allow to emphasize
hidden group structures in networks or focus on specific nodes. The package is best used in conjunction with
ggraph.
Some features of the package are:
\itemize{
\item \code{layout_with_stress()} is a state of the art deterministic layout algorithms.
\item \code{layout_as_backbone()} uncovers hidden group structures (if they exist) by emphasizing strongly embedded edges.
\item \code{layout_with_focus()} and \code{layout_with_centrality()} produce concentric layouts with a focal or most central nodes in the center.
\item \code{layout_with_eigen()} implements some layout algorithms on the basis of eigenvectors
\item \code{layout_with_sparse_stress()} sparse stress for large graphs
\item \code{layout_with_pmds()} pivot MDS for large graphs.
\item \code{layout_as_dynamic()} for longitudinal network data
}
A detailed tutorial can be found at \url{https://schochastics.github.io/netVizR/}
}
\seealso{
Useful links:
\itemize{
\item \url{https://github.com/schochastics/graphlayouts}
\item \url{https://schochastics.github.io/graphlayouts/}
\item Report bugs at \url{https://github.com/schochastics/graphlayouts/issues}
}
}
\author{
\strong{Maintainer}: David Schoch \email{david@schochastics.net} (\href{https://orcid.org/0000-0003-2952-4812}{ORCID})
}
\keyword{internal}
graphlayouts/man/layout_dynamic.Rd 0000644 0001762 0000144 00000003542 14744174674 017105 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_dynamic.R
\name{layout_dynamic}
\alias{layout_dynamic}
\alias{layout_as_dynamic}
\title{dynamic graph layout}
\usage{
layout_as_dynamic(gList, weights = NA, alpha = 0.5, iter = 500, tol = 1e-04)
}
\arguments{
\item{gList}{list of igraph objects. Each network must contain the same set of nodes.}
\item{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.}
\item{alpha}{weighting of reference layout. See details.}
\item{iter}{number of iterations during stress optimization}
\item{tol}{stopping criterion for stress optimization}
}
\value{
list of coordinates for each graph
}
\description{
Create layouts for longitudinal networks.
}
\details{
The reference layout is calculated based on the union of all graphs. The parameter alpha controls the influence of the reference layout.
For alpha=1, only the reference layout is used and all graphs have the same layout. For alpha=0, the stress layout of each individual graph is used. Values in-between interpolate between the two layouts.
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).
}
\examples{
library(igraph)
g1 <- sample_gnp(20, 0.2)
g2 <- sample_gnp(20, 0.2)
g3 <- sample_gnp(20, 0.2)
xy <- layout_as_dynamic(list(g1, g2, g3))
# layout for first network
xy[[1]]
}
\references{
Brandes, U. and Indlekofer, N. and Mader, M. (2012). Visualization methods for longitudinal social networks and stochastic actor-oriented modeling. \emph{Social Networks} 34 (3) 291-308
}
graphlayouts/man/layout_manipulate.Rd 0000644 0001762 0000144 00000001504 14744174674 017614 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_manipulate.R
\name{layout_manipulate}
\alias{layout_manipulate}
\alias{layout_rotate}
\alias{layout_mirror}
\title{manipulate layout}
\usage{
layout_rotate(xy, angle)
layout_mirror(xy, axis = "vertical")
}
\arguments{
\item{xy}{graph layout}
\item{angle}{angle for rotation}
\item{axis}{mirror horizontal or vertical}
}
\value{
manipulated matrix of xy coordinates
}
\description{
functions to manipulate an existing layout
}
\details{
These functions are mostly useful for deterministic layouts such as \link{layout_with_stress}
}
\examples{
library(igraph)
g <- sample_gnp(50, 0.3)
xy <- layout_with_stress(g)
# rotate 90 degrees
xy <- layout_rotate(xy, 90)
# flip horizontally
xy <- layout_mirror(xy, "horizontal")
}
\author{
David Schoch
}
graphlayouts/man/layout_centrality_group.Rd 0000644 0001762 0000144 00000002140 14744174674 021044 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_stress.R, R/layouts.R
\name{layout_centrality_group}
\alias{layout_centrality_group}
\alias{layout_with_centrality_group}
\alias{layout_igraph_centrality_group}
\title{radial centrality group layout}
\usage{
layout_with_centrality_group(g, cent, group, shrink = 10, ...)
layout_igraph_centrality_group(g, cent, group, shrink = 10, circular, ...)
}
\arguments{
\item{g}{igraph object}
\item{cent}{centrality scores}
\item{group}{vector indicating grouping of nodes}
\item{shrink}{shrink the reserved angle range for a group to increase the gaps between groups}
\item{...}{additional arguments to layout_with_centrality
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.}
\item{circular}{not used}
}
\value{
matrix of xy coordinates
}
\description{
arranges nodes in concentric circles according to a centrality index and keeping groups within a angle range
}
\examples{
library(igraph)
}
\seealso{
\link{layout_centrality}
}
graphlayouts/man/layout_umap.Rd 0000644 0001762 0000144 00000002341 14744174674 016417 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_umap.R, R/layouts.R
\name{layout_umap}
\alias{layout_umap}
\alias{layout_with_umap}
\alias{layout_igraph_umap}
\title{UMAP graph layouts}
\usage{
layout_with_umap(g, pivots = NULL, ...)
layout_igraph_umap(g, circular, ...)
}
\arguments{
\item{g}{igraph object}
\item{pivots}{if not NULL, number of pivot nodes to use for distance calculation (for large graphs).}
\item{...}{additional parameters for umap. See the \code{?uwot::umap} for help.}
\item{circular}{not used}
}
\value{
matrix of xy coordinates
}
\description{
Using the UMAP dimensionality reduction algorithm as a graph layout
}
\details{
The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. UMAP can be tuned by many different parameters. Refer to the documentation at https://github.com/jlmelville/uwot for help
}
\examples{
library(igraph)
g <- sample_islands(10, 20, 0.6, 10)
# xy <- layout_with_umap(g, min_dist = 0.5)
}
\references{
McInnes, Leland, John Healy, and James Melville. "Umap: Uniform manifold approximation and projection for dimension reduction." arXiv preprint arXiv:1802.03426 (2018).
}
\author{
David Schoch
}
graphlayouts/man/layout_spectral.Rd 0000644 0001762 0000144 00000002333 14744174674 017273 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_spectral.R, R/layouts.R
\name{layout_spectral}
\alias{layout_spectral}
\alias{layout_with_eigen}
\alias{layout_igraph_eigen}
\title{spectral graph layouts}
\usage{
layout_with_eigen(g, type = "laplacian", ev = "smallest")
layout_igraph_eigen(g, type = "laplacian", ev = "smallest", circular)
}
\arguments{
\item{g}{igraph object}
\item{type}{matrix to be used for spectral decomposition. either 'adjacency' or 'laplacian'}
\item{ev}{eigenvectors to be used. Either 'smallest' or 'largest'.}
\item{circular}{not used}
}
\value{
matrix of xy coordinates
}
\description{
Using a set of eigenvectors of matrices associated with a graph as coordinates
}
\details{
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.
}
\examples{
library(igraph)
g <- sample_gnp(50, 0.2)
xy <- layout_with_eigen(g, type = "adjacency", ev = "largest")
xy <- layout_with_eigen(g, type = "adjacency", ev = "smallest")
xy <- layout_with_eigen(g, type = "laplacian", ev = "largest")
xy <- layout_with_eigen(g, type = "laplacian", ev = "smallest")
}
\author{
David Schoch
}
graphlayouts/man/draw_circle.Rd 0000644 0001762 0000144 00000001611 14744174674 016335 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/annotate_functions.R
\name{draw_circle}
\alias{draw_circle}
\title{Draw concentric circles}
\usage{
draw_circle(col = "#00BFFF", use = "focus", max.circle)
}
\arguments{
\item{col}{color of circles}
\item{use}{one of 'focus' or 'cent'}
\item{max.circle}{if use = 'focus' specifies the number of circles to draw}
}
\value{
concentric circles around origin
}
\description{
Draw concentric circles
}
\details{
this function is best used with a concentric layout such as \link{layout_with_focus} and \link{layout_with_centrality}.
}
\examples{
library(igraph)
g <- sample_gnp(10, 0.4)
\dontrun{
library(ggraph)
ggraph(g, layout = "centrality", centrality = degree(g)) +
draw_circle(use = "cent") +
geom_edge_link() +
geom_node_point(shape = 21, fill = "grey25", size = 5) +
theme_graph() +
coord_fixed()
}
}
graphlayouts/man/layout_focus.Rd 0000644 0001762 0000144 00000003324 14744174674 016576 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_stress.R, R/layouts.R
\name{layout_focus}
\alias{layout_focus}
\alias{layout_with_focus}
\alias{layout_igraph_focus}
\title{radial focus layout}
\usage{
layout_with_focus(g, v, weights = NA, iter = 500, tol = 1e-04)
layout_igraph_focus(g, v, weights = NA, iter = 500, tol = 1e-04, circular)
}
\arguments{
\item{g}{igraph object}
\item{v}{id of focal node to be placed in the center}
\item{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.}
\item{iter}{number of iterations during stress optimization}
\item{tol}{stopping criterion for stress optimization}
\item{circular}{not used}
}
\value{
a list containing xy coordinates and the distances to the focal node
}
\description{
arrange nodes in concentric circles around a focal node according to their distance from the focus.
}
\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).
}
\examples{
library(igraph)
g <- sample_gnp(10, 0.4)
coords <- layout_with_focus(g, v = 1)
coords
}
\references{
Brandes, U., & Pich, C. (2011). More flexible radial layout. \emph{Journal of Graph Algorithms and Applications}, 15(1), 157-173.
}
\seealso{
\link{layout_focus_group}
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.
}
graphlayouts/man/layout_sparse_stress.Rd 0000644 0001762 0000144 00000002442 14744174674 020357 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_large_graphs.R, R/layouts.R
\name{layout_sparse_stress}
\alias{layout_sparse_stress}
\alias{layout_with_sparse_stress}
\alias{layout_igraph_sparse_stress}
\title{sparse stress graph layout}
\usage{
layout_with_sparse_stress(g, pivots, weights = NA, iter = 500)
layout_igraph_sparse_stress(g, pivots, weights = NA, iter = 500, circular)
}
\arguments{
\item{g}{igraph object}
\item{pivots}{number of pivots}
\item{weights}{ignored}
\item{iter}{number of iterations during stress optimization}
\item{circular}{not used}
}
\value{
matrix of xy coordinates
}
\description{
stress majorization for larger graphs based on a set of pivot nodes.
}
\details{
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.
}
\examples{
\dontrun{
library(igraph)
library(ggraph)
g <- sample_gnp(1000, 0.005)
ggraph(g, layout = "sparse_stress", pivots = 100) +
geom_edge_link0(edge_colour = "grey66") +
geom_node_point(shape = 21, fill = "grey25", size = 5) +
theme_graph()
}
}
\references{
Ortmann, M. and Klimenta, M. and Brandes, U. (2016). A Sparse Stress Model. https://arxiv.org/pdf/1608.08909.pdf
}
\author{
David Schoch
}
graphlayouts/man/layout_stress.Rd 0000644 0001762 0000144 00000004270 14744174674 017003 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_stress.R, R/layouts.R
\name{layout_stress}
\alias{layout_stress}
\alias{layout_with_stress}
\alias{layout_igraph_stress}
\title{stress majorization layout}
\usage{
layout_with_stress(
g,
weights = NA,
iter = 500,
tol = 1e-04,
mds = TRUE,
bbox = 30
)
layout_igraph_stress(
g,
weights = NA,
iter = 500,
tol = 1e-04,
mds = TRUE,
bbox = 30,
circular
)
}
\arguments{
\item{g}{igraph object}
\item{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.}
\item{iter}{number of iterations during stress optimization}
\item{tol}{stopping criterion for stress optimization}
\item{mds}{should an MDS layout be used as initial layout (default: TRUE)}
\item{bbox}{width of layout. Only relevant to determine the placement of disconnected graphs}
\item{circular}{not used}
}
\value{
matrix of xy coordinates
}
\description{
force-directed graph layout based on stress majorization.
Similar to Kamada-Kawai, but generally faster and with better results.
}
\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.
}
\examples{
library(igraph)
set.seed(665)
g <- sample_pa(100, 1, 1, directed = FALSE)
# calculate layout manually
xy <- layout_with_stress(g)
# use it with ggraph
\dontrun{
library(ggraph)
ggraph(g, layout = "stress") +
geom_edge_link0(edge_width = 0.2, colour = "grey") +
geom_node_point(col = "black", size = 0.3) +
theme_graph()
}
}
\references{
Gansner, E. R., Koren, Y., & North, S. (2004). Graph drawing by stress majorization. \emph{In International Symposium on Graph Drawing} (pp. 239-250). Springer, Berlin, Heidelberg.
}
\seealso{
\link{layout_stress3D}
}
graphlayouts/man/metro_berlin.Rd 0000644 0001762 0000144 00000000710 14744174674 016537 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-examples.R
\docType{data}
\name{metro_berlin}
\alias{metro_berlin}
\title{Subway network of Berlin}
\format{
igraph object
}
\usage{
metro_berlin
}
\description{
A dataset containing the subway network of Berlin
}
\references{
Kujala, Rainer, et al. "A collection of public transport network data sets for 25 cities." Scientific data 5 (2018): 180089.
}
\keyword{datasets}
graphlayouts/man/layout_fixed_coords.Rd 0000644 0001762 0000144 00000004020 14744174674 020121 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_stress.R, R/layouts.R
\name{layout_fixed_coords}
\alias{layout_fixed_coords}
\alias{layout_with_fixed_coords}
\alias{layout_igraph_fixed_coords}
\title{Layout with fixed coordinates}
\usage{
layout_with_fixed_coords(
g,
coords,
weights = NA,
iter = 500,
tol = 1e-04,
mds = TRUE,
bbox = 30
)
layout_igraph_fixed_coords(
g,
coords,
weights = NA,
iter = 500,
tol = 1e-04,
mds = TRUE,
bbox = 30,
circular
)
}
\arguments{
\item{g}{igraph object}
\item{coords}{numeric n x 2 matrix, where n is the number of nodes. values
are either NA or fixed coordinates. coordinates are only calculated for the
NA values.}
\item{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.}
\item{iter}{number of iterations during stress optimization}
\item{tol}{stopping criterion for stress optimization}
\item{mds}{should an MDS layout be used as initial layout (default: TRUE)}
\item{bbox}{constrain dimension of output. Only relevant to determine the placement of disconnected graphs}
\item{circular}{not used}
}
\value{
matrix of xy coordinates
}
\description{
force-directed graph layout based on stress majorization with
fixed coordinates for some nodes
}
\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.
}
\examples{
library(igraph)
set.seed(12)
g <- sample_bipartite(10, 5, "gnp", 0.5)
fxy <- cbind(c(rep(0, 10), rep(1, 5)), NA)
xy <- layout_with_fixed_coords(g, fxy)
}
\seealso{
\link{layout_constrained_stress}
}
graphlayouts/man/layout_pmds.Rd 0000644 0001762 0000144 00000003430 14744174674 016420 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_large_graphs.R, R/layouts.R
\name{layout_pmds}
\alias{layout_pmds}
\alias{layout_with_pmds}
\alias{layout_igraph_pmds}
\title{pivot MDS graph layout}
\usage{
layout_with_pmds(g, pivots, weights = NA, D = NULL, dim = 2)
layout_igraph_pmds(g, pivots, weights = NA, D = NULL, circular)
}
\arguments{
\item{g}{igraph object}
\item{pivots}{number of pivots}
\item{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.}
\item{D}{precomputed distances from pivots to all nodes (if available, default: NULL)}
\item{dim}{dimensionality of layout (defaults to 2)}
\item{circular}{not used}
}
\value{
matrix of coordinates
}
\description{
similar to \link[igraph]{layout_with_mds} but uses only a small set of pivots for MDS. Considerably faster than MDS and thus applicable for larger graphs.
}
\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.
}
\examples{
\dontrun{
library(igraph)
library(ggraph)
g <- sample_gnp(1000, 0.01)
xy <- layout_with_pmds(g, pivots = 100)
}
}
\references{
Brandes, U. and Pich, C. (2006). Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. In \emph{International Symposium on Graph Drawing} (pp. 42-53). Springer
}
\author{
David Schoch
}
graphlayouts/man/multilvl_ex.Rd 0000644 0001762 0000144 00000000522 14744174674 016423 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-examples.R
\docType{data}
\name{multilvl_ex}
\alias{multilvl_ex}
\title{Multilevel example Network}
\format{
igraph object
}
\usage{
multilvl_ex
}
\description{
Multilevel network, where both levels have different structural features
}
\keyword{datasets}
graphlayouts/man/annotate_circle.Rd 0000644 0001762 0000144 00000002076 14744174674 017217 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/annotate_functions.R
\name{annotate_circle}
\alias{annotate_circle}
\title{annotate concentric circles}
\usage{
annotate_circle(cent, col = "#00BFFF", format = "", pos = "top", text_size = 3)
}
\arguments{
\item{cent}{centrality scores used for layout}
\item{col}{color of text}
\item{format}{either empty string or 'scientific'}
\item{pos}{position of text ('top' or 'bottom')}
\item{text_size}{font size for annotations}
}
\value{
annotated concentric circles around origin
}
\description{
annotate concentric circles
}
\details{
this function is best used with \link{layout_with_centrality} together with \link{draw_circle}.
}
\examples{
library(igraph)
g <- sample_gnp(10, 0.4)
\dontrun{
library(ggraph)
ggraph(g, layout = "centrality", centrality = closeness(g)) +
draw_circle(use = "cent") +
annotate_circle(closeness(g), pos = "bottom", format = "scientific") +
geom_edge_link() +
geom_node_point(shape = 21, fill = "grey25", size = 5) +
theme_graph() +
coord_fixed()
}
}
graphlayouts/man/layout_multilevel.Rd 0000644 0001762 0000144 00000005663 14744405056 017640 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_multilevel.R, R/layouts.R
\name{layout_multilevel}
\alias{layout_multilevel}
\alias{layout_as_multilevel}
\alias{layout_igraph_multilevel}
\title{multilevel layout}
\usage{
layout_as_multilevel(
g,
type = "all",
FUN1,
FUN2,
params1 = NULL,
params2 = NULL,
ignore_iso = TRUE,
project2D = TRUE,
alpha = 35,
beta = 45
)
layout_igraph_multilevel(
g,
type = "all",
FUN1,
FUN2,
params1 = NULL,
params2 = NULL,
ignore_iso = TRUE,
alpha = 35,
beta = 45,
circular
)
}
\arguments{
\item{g}{igraph object. Must have a vertex attribute "lvl" which is 1 or 2.}
\item{type}{one of "all", "separate","fix1" or "fix2". see details}
\item{FUN1}{if type="separate", the layout function to be used for level 1}
\item{FUN2}{if type="separate", the layout function to be used for level 2}
\item{params1}{named list of parameters for FUN1}
\item{params2}{named list of parameters for FUN2}
\item{ignore_iso}{treatment of isolates within levels. see details}
\item{project2D}{logical. Defaults to TRUE (project to 2D).}
\item{alpha}{angle for isometric projection between 0 and 90}
\item{beta}{angle for isometric projection between 0 and 90}
\item{circular}{not used}
}
\value{
matrix of xy coordinates
}
\description{
Layout algorithm to visualize multilevel networks
}
\details{
The algorithm internally computes a 3D layout where each level is in a separate y-plane.
The layout is then projected into 2D via an isometric mapping, controlled by the parameters
\code{alpha} and \code{beta}. It may take some adjusting to \code{alpha} and \code{beta} to find a good perspective.
If type="all", the layout is computed at once for the complete network.
For type="separate", two user specified layout algorithms (\code{FUN1} and \code{FUN2}) are used for the levels.
The named lists \code{param1} and \code{param2} can be used to set parameters for \code{FUN1} and \code{FUN2}.
This option helpful for situations where different structural features of the levels should be emphasized.
For type="fix1" and type="fix2" only one of the level layouts is fixed. The other one is calculated by optimizing the
inter level ties, such that they are drawn (almost) vertical.
The \code{ignore_iso} parameter controls the handling of isolates. If TRUE, nodes without inter level edges are ignored during the layout process
and added at the end. If FALSE they are left unchanged
The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'.
}
\examples{
library(igraph)
data("multilvl_ex")
\dontrun{
# compute a layout for the whole network
xy <- layout_as_multilevel(multilvl_ex, type = "all", alpha = 25, beta = 45)
# compute a layout for each level separately and combine them
xy <- layout_as_multilevel(multilvl_ex,
type = "separate",
FUN1 = layout_as_backbone,
FUN2 = layout_with_stress,
alpha = 25, beta = 45
)
}
}
graphlayouts/man/layout_stress3D.Rd 0000644 0001762 0000144 00000002723 14744174674 017173 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_stress.R
\name{layout_stress3D}
\alias{layout_stress3D}
\alias{layout_with_stress3D}
\title{stress majorization layout in 3D}
\usage{
layout_with_stress3D(
g,
weights = NA,
iter = 500,
tol = 1e-04,
mds = TRUE,
bbox = 30
)
}
\arguments{
\item{g}{igraph object}
\item{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.}
\item{iter}{number of iterations during stress optimization}
\item{tol}{stopping criterion for stress optimization}
\item{mds}{should an MDS layout be used as initial layout (default: TRUE)}
\item{bbox}{width of layout. Only relevant to determine the placement of disconnected graphs}
}
\value{
matrix of xyz coordinates
}
\description{
force-directed graph layout based on stress majorization in 3D.
}
\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).
}
\references{
Gansner, E. R., Koren, Y., & North, S. (2004). Graph drawing by stress majorization. \emph{In International Symposium on Graph Drawing} (pp. 239-250). Springer, Berlin, Heidelberg.
}
\seealso{
\link{layout_stress}
}
graphlayouts/man/layout_focus_group.Rd 0000644 0001762 0000144 00000003544 14744174674 020016 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layout_stress.R, R/layouts.R
\name{layout_focus_group}
\alias{layout_focus_group}
\alias{layout_with_focus_group}
\alias{layout_igraph_focus_group}
\title{radial focus group layout}
\usage{
layout_with_focus_group(
g,
v,
group,
shrink = 10,
weights = NA,
iter = 500,
tol = 1e-04
)
layout_igraph_focus_group(
g,
v,
group,
shrink = 10,
weights = NA,
iter = 500,
tol = 1e-04,
circular
)
}
\arguments{
\item{g}{igraph object}
\item{v}{id of focal node to be placed in the center}
\item{group}{vector indicating grouping of nodes}
\item{shrink}{shrink the reserved angle range for a group to increase the gaps between groups}
\item{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.}
\item{iter}{number of iterations during stress optimization}
\item{tol}{stopping criterion for stress optimization}
\item{circular}{not used}
}
\value{
matrix of xy coordinates
}
\description{
arrange nodes in concentric circles around a focal node according to their distance from the focus and keep predefined groups in the same angle range.
}
\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).
}
\examples{
library(igraph)
g <- sample_islands(4, 5, 0.8, 2)
grp <- as.character(rep(1:4, each = 5))
layout_with_focus_group(g, v = 1, group = grp, shrink = 10)
}
\seealso{
\link{layout_focus}
The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'.
}
graphlayouts/man/figures/ 0000755 0001762 0000144 00000000000 14744443064 015225 5 ustar ligges users graphlayouts/man/figures/README-flex_focus-1.png 0000644 0001762 0000144 00000204367 14744174674 021205 0 ustar ligges users PNG
IHDR Hc pHYs od IDATxw|TU?>I EHB"}(
]u|\u]e)DwB&!Hz11 3sG2{f"@!aGB{B QB!DO!H$8nE *YeT.S 29 DPx7n6
Һȟ6a Zۀ>NfeTĘ@n)LN S|ravhDUXB eyQ
'
\]$VgjkJ. 9 .BCZ(k"L\STqi '8tF.6nGS64ho,
,䘎OrL ?%+?`br
4N^
Q\1j L 6#VV#v#V E%r7*``r63ShLB i !DfST\/N)Q