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[](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://www.mr.schochastics.net/material/netVizR/).**
The package implements the following algorithms:
- Stress majorization
([Paper](https://graphviz.gitlab.io/_pages/Documentation/GKN04.pdf))
- Quadrilateral backbone layout
([Paper](https://jgaa.info/accepted/2015/NocajOrtmannBrandes2015.19.2.pdf))
- flexible radial layouts
([Paper](https://jgaa.info/accepted/2011/BrandesPich2011.15.1.pdf))
- sparse stress ([Paper](https://arxiv.org/abs/1608.08909))
- pivot MDS
([Paper](https://kops.uni-konstanz.de/bitstream/handle/123456789/5741/bp_empmdsld_06.pdf?sequence=1&isAllowed=y))
- dynamic layout for longitudinal data
([Paper](https://kops.uni-konstanz.de/bitstream/handle/123456789/20924/Brandes_209246.pdf?sequence=2))
- 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)$grp <- as.character(rep(1:9, each = 40))
ggraph(g, layout = "stress") +
geom_edge_link0(colour = rgb(0, 0, 0, 0.5), width = 0.1) +
geom_node_point(aes(col = grp)) +
scale_color_brewer(palette = "Set1") +
theme_graph() +
theme(legend.position = "none")
```
The backbone layout helps to uncover potential group structures based on
edge embeddedness and puts more emphasis on this structure in the
layout.
To use the function, you need to install the package `oaqc`
``` r
install.packages("oaqc")
```
``` r
bb <- layout_as_backbone(g, keep = 0.4)
E(g)$col <- F
E(g)$col[bb$backbone] <- T
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.
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