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https://github.com/schochastics/graphlayouts
new layout algorithms for network visualizations in R
https://github.com/schochastics/graphlayouts
ggraph graph-algorithms network-analysis network-visualization r
Last synced: 3 months ago
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new layout algorithms for network visualizations in R
- Host: GitHub
- URL: https://github.com/schochastics/graphlayouts
- Owner: schochastics
- License: other
- Created: 2018-09-12T20:03:02.000Z (almost 6 years ago)
- Default Branch: main
- Last Pushed: 2024-03-11T20:33:03.000Z (4 months ago)
- Last Synced: 2024-03-24T16:32:02.323Z (3 months ago)
- Topics: ggraph, graph-algorithms, network-analysis, network-visualization, r
- Language: R
- Homepage: https://schochastics.github.io/graphlayouts/
- Size: 19.8 MB
- Stars: 262
- Watchers: 9
- Forks: 16
- Open Issues: 4
-
Metadata Files:
- Readme: README.Rmd
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Lists
- awesome-r-dataviz - graphlayouts - New layout algorithms for network visualizations in R. (Network Visualization / Miscellaneous)
- awesome-stars - schochastics/graphlayouts - new layout algorithms for network visualizations in R (R)
README
---
output: github_document
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
fig.align = "center",
out.width = "80%",
comment = "#>",
fig.path = "man/figures/README-",
echo = TRUE,
warning = FALSE,
message = FALSE
)
```
# graphlayouts 
[](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://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 install,eval=FALSE}
# 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 example}
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()
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 example_un}
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()
ggraph(g, layout = "stress", bbox = 40) +
geom_edge_link0() +
geom_node_point() +
theme_graph()
```
## Backbone Layout
Backbone layouts are helpful for drawing hairballs.
```{r hairball,eval = FALSE}
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_oaqc, eval=FALSE}
install.packages("oaqc")
```
```{r backbone,eval=FALSE}
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 flex_focus,eval=FALSE}
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 flex_cent}
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 dynamic,eval = FALSE}
# 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.