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.
The package implements the following algorithms:
- Stress majorization (Paper)
- Quadrilateral backbone layout (Paper)
- flexible radial layouts (Paper)
- sparse stress (Paper)
- pivot MDS (Paper)
- dynamic layout for longitudinal data (Paper)
- 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
# dev version
remotes::install_github("schochastics/graphlayouts")
# CRAN
install.packages("graphlayouts")This example is a bit of a special case since it exploits some weird issues in igraph.
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 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.
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 layouts are helpful for drawing hairballs.
set.seed(665)
# create network with a group structure
g <- sample_islands(9, 40, 0.4, 15)
g <- simplify(g)
V(g)$grp <- as.character(rep(1:9, each = 40))
ggraph(g, layout = "stress") +
geom_edge_link0(colour = rgb(0, 0, 0, 0.5), width = 0.1) +
geom_node_point(aes(col = grp)) +
scale_color_brewer(palette = "Set1") +
theme_graph() +
theme(legend.position = "none")
The backbone layout helps to uncover potential group structures based on edge embeddedness and puts more emphasis on this structure in the layout.
bb <- layout_as_backbone(g, keep = 0.4)
E(g)$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")
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.
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
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.
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
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
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()
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.
# 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)
The functions layout_mirror() and layout_rotate() can be used to
manipulate an existing layout
Simply open an issue on GitHub.
If you have an idea (but no code yet), open an issue 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.
The easiest way is to open an issue - this way, your question is also visible to others who may face similar problems.
Please note that the graphlayouts project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.