graphlayouts

graphlayouts: Layout algorithms for network visualizations in R - Published in JOSS (2023)

https://github.com/schochastics/graphlayouts

Keywords

Keywords from Contributors

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new layout algorithms for network visualizations in R

README.Rmd

---
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  

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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 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.

```{r backbone,eval=FALSE}
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 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.

Owner

JOSS Publication

graphlayouts: Layout algorithms for network visualizations in R

Published

April 29, 2023

DOI

10.21105/joss.05238

Volume 8, Issue 84, Page 5238

Authors

David Schoch
GESIS - Leibniz Institute for the Social Sciences

Editor

Chris Vernon

Tags

network visualization graph drawing layout algorithms

View PDF Review Thread Software Archive

Citation (CITATION.cff)

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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "graphlayouts" in publications use:'
type: software
license: MIT
title: 'graphlayouts: Additional Layout Algorithms for Network Visualizations'
version: 1.2.1
doi: 10.21105/joss.05238
identifiers:
- type: doi
  value: 10.32614/CRAN.package.graphlayouts
abstract: Several new layout algorithms to visualize networks are provided which are
  not part of 'igraph'. Most are based on the concept of stress majorization by Gansner
  et al. (2004) <https://doi.org/10.1007/978-3-540-31843-9_25>. Some more specific
  algorithms allow the user to emphasize hidden group structures in networks or focus
  on specific nodes.
authors:
- family-names: Schoch
  given-names: David
  email: david@schochastics.net
  orcid: https://orcid.org/0000-0003-2952-4812
preferred-citation:
  type: article
  title: 'graphlayouts: Layout algorithms for network visualizations in R'
  authors:
  - name: David Schoch
  year: '2023'
  publisher:
    name: The Open Journal
  volume: '8'
  issue: '84'
  journal: Journal of Open Source Software
  doi: 10.21105/joss.05238
  url: https://doi.org/10.21105/joss.05238
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repository: https://CRAN.R-project.org/package=graphlayouts
repository-code: https://github.com/schochastics/graphlayouts
url: https://schochastics.github.io/graphlayouts/
contact:
- family-names: Schoch
  given-names: David
  email: david@schochastics.net
  orcid: https://orcid.org/0000-0003-2952-4812
keywords:
- ggraph
- graph-algorithms
- network-analysis
- network-visualization
- r
references:
- type: software
  title: 'R: A Language and Environment for Statistical Computing'
  notes: Depends
  url: https://www.R-project.org/
  authors:
  - name: R Core Team
  institution:
    name: R Foundation for Statistical Computing
    address: Vienna, Austria
  year: '2024'
  version: '>= 3.6.0'
- type: software
  title: igraph
  abstract: 'igraph: Network Analysis and Visualization'
  notes: Imports
  url: https://r.igraph.org/
  repository: https://CRAN.R-project.org/package=igraph
  authors:
  - family-names: Csárdi
    given-names: Gábor
    email: csardi.gabor@gmail.com
    orcid: https://orcid.org/0000-0001-7098-9676
  - family-names: Nepusz
    given-names: Tamás
    email: ntamas@gmail.com
    orcid: https://orcid.org/0000-0002-1451-338X
  - family-names: Traag
    given-names: Vincent
    orcid: https://orcid.org/0000-0003-3170-3879
  - family-names: Horvát
    given-names: Szabolcs
    email: szhorvat@gmail.com
    orcid: https://orcid.org/0000-0002-3100-523X
  - family-names: Zanini
    given-names: Fabio
    email: fabio.zanini@unsw.edu.au
    orcid: https://orcid.org/0000-0001-7097-8539
  - family-names: Noom
    given-names: Daniel
  - family-names: Müller
    given-names: Kirill
    email: kirill@cynkra.com
    orcid: https://orcid.org/0000-0002-1416-3412
  year: '2024'
  doi: 10.32614/CRAN.package.igraph
  version: '>= 2.0.0'
- type: software
  title: Rcpp
  abstract: 'Rcpp: Seamless R and C++ Integration'
  notes: Imports
  url: https://www.rcpp.org
  repository: https://CRAN.R-project.org/package=Rcpp
  authors:
  - family-names: Eddelbuettel
    given-names: Dirk
    email: edd@debian.org
    orcid: https://orcid.org/0000-0001-6419-907X
  - family-names: Francois
    given-names: Romain
    orcid: https://orcid.org/0000-0002-2444-4226
  - family-names: Allaire
    given-names: JJ
    orcid: https://orcid.org/0000-0003-0174-9868
  - family-names: Ushey
    given-names: Kevin
    orcid: https://orcid.org/0000-0003-2880-7407
  - family-names: Kou
    given-names: Qiang
    orcid: https://orcid.org/0000-0001-6786-5453
  - family-names: Russell
    given-names: Nathan
  - family-names: Ucar
    given-names: Iñaki
    orcid: https://orcid.org/0000-0001-6403-5550
  - family-names: Bates
    given-names: Doug
    orcid: https://orcid.org/0000-0001-8316-9503
  - family-names: Chambers
    given-names: John
  year: '2024'
  doi: 10.32614/CRAN.package.Rcpp
- type: software
  title: testthat
  abstract: 'testthat: Unit Testing for R'
  notes: Suggests
  url: https://testthat.r-lib.org
  repository: https://CRAN.R-project.org/package=testthat
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2024'
  doi: 10.32614/CRAN.package.testthat
- type: software
  title: ggplot2
  abstract: 'ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics'
  notes: Suggests
  url: https://ggplot2.tidyverse.org
  repository: https://CRAN.R-project.org/package=ggplot2
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
    orcid: https://orcid.org/0000-0003-4757-117X
  - family-names: Chang
    given-names: Winston
    orcid: https://orcid.org/0000-0002-1576-2126
  - family-names: Henry
    given-names: Lionel
  - family-names: Pedersen
    given-names: Thomas Lin
    email: thomas.pedersen@posit.co
    orcid: https://orcid.org/0000-0002-5147-4711
  - family-names: Takahashi
    given-names: Kohske
  - family-names: Wilke
    given-names: Claus
    orcid: https://orcid.org/0000-0002-7470-9261
  - family-names: Woo
    given-names: Kara
    orcid: https://orcid.org/0000-0002-5125-4188
  - family-names: Yutani
    given-names: Hiroaki
    orcid: https://orcid.org/0000-0002-3385-7233
  - family-names: Dunnington
    given-names: Dewey
    orcid: https://orcid.org/0000-0002-9415-4582
  - family-names: Brand
    given-names: Teun
    name-particle: van den
    orcid: https://orcid.org/0000-0002-9335-7468
  year: '2024'
  doi: 10.32614/CRAN.package.ggplot2
- type: software
  title: RcppArmadillo
  abstract: 'RcppArmadillo: ''Rcpp'' Integration for the ''Armadillo'' Templated Linear
    Algebra Library'
  notes: LinkingTo
  url: https://dirk.eddelbuettel.com/code/rcpp.armadillo.html
  repository: https://CRAN.R-project.org/package=RcppArmadillo
  authors:
  - family-names: Eddelbuettel
    given-names: Dirk
    email: edd@debian.org
    orcid: https://orcid.org/0000-0001-6419-907X
  - family-names: Francois
    given-names: Romain
    orcid: https://orcid.org/0000-0002-2444-4226
  - family-names: Bates
    given-names: Doug
    orcid: https://orcid.org/0000-0001-8316-9503
  - family-names: Ni
    given-names: Binxiang
  - family-names: Sanderson
    given-names: Conrad
    orcid: https://orcid.org/0000-0002-0049-4501
  year: '2024'
  doi: 10.32614/CRAN.package.RcppArmadillo

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