graphlayouts

An R package for network visualization

This package implements some graph layout algorithms that are not available in igraph.

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

Install

# dev version
remotes::install_github("schochastics/graphlayouts")

#CRAN
install.packages("graphlayouts")

Stress Majorization

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 = F),
  sample_pa(20,directed = F),
  sample_pa(30,directed = F),
  sample_pa(40,directed = F),
  sample_pa(50,directed = F),
  sample_pa(60,directed = F),
  sample_pa(80,directed = F)
)

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.

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)$col <- F
E(g)$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.

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.

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
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 co-citation network with 12k nodes and 68k edges