The package implements some graph layout algorithms that are not available in the igraph
package.
The package implements three algorithms which serve different purposes:
This example is a bit of a special case since it exploits some weird issues in igraph.
library(igraph)
#>
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
#>
#> decompose, spectrum
#> The following object is masked from 'package:base':
#>
#> union
library(ggraph)
#> Loading required package: ggplot2
library(graphlayouts)
set.seed(666)
pa <- sample_pa(1000,1,1,directed = F)
ggraph(pa)+
geom_edge_link(width=0.2,colour="grey")+
geom_node_point(col="black",size=0.3)+
theme_graph()
#> Using `nicely` as default layout
ggraph(pa,layout="stress")+
geom_edge_link(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) +
geom_edge_link() +
geom_node_point() +
theme_graph()
#> Using `nicely` as default layout
Backbone layouts are helpful for drawing so called hairballs.
set.seed(665)
#create network with a group structure
g <- sample_islands(9,40,0.4,15)
g <- simplify(g)
V(g)$grp <- as.character(rep(1:9,each=40))
ggraph(g,layout="stress")+
geom_edge_link(colour=rgb(0,0,0,0.5),width=0.1)+
geom_node_point(aes(col=grp))+
scale_color_brewer(palette = "Set1")+
theme_graph()+
theme(legend.position = "none")
The backbone layout helps to uncover potential group structures based on edge embeddedness and puts more emphasis on this structure in the layout.
bb <- layout_as_backbone(g,keep=0.4)
E(g)$col <- F
E(g)$col[bb$backbone] <- T
ggraph(g,layout="manual",node.positions=data.frame(x=bb$xy[,1],y=bb$xy[,2]))+
geom_edge_link(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)
#data("karate")
p1 <- ggraph(karate,layout = "focus",v = 1) +
draw_circle(use = "focus",max.circle = 3)+
geom_edge_link(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",v = 34) +
draw_circle(use = "focus",max.circle = 4)+
geom_edge_link(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
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.
bc <- betweenness(karate)
p1 <- ggraph(karate,layout = "centrality", cent = bc, tseq = seq(0,1,0.15)) +
draw_circle(use = "cent") +
annotate_circle(bc,format="",pos="bottom") +
geom_edge_link(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", cent = cc, tseq = seq(0,1,0.2)) +
draw_circle(use = "cent") +
annotate_circle(cc,format="scientific",pos="bottom") +
geom_edge_link(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
Spectral layouts use eigenvectors of matrices associated with a graph for layouting. Possible matrices are the adjacency matrix and the laplacian matrix. Additionally, one can choose if the largest or smallest eigenvectors should be used.
g <- sample_gnp(50,0.2)
ggraph(g,layout="eigen",type="adjacency",ev="largest")+
geom_edge_link(n=2,edge_colour="grey66")+
geom_node_point(shape=21,fill="grey25",size=5)+
theme_graph()
ggraph(g,layout="eigen",type="adjacency",ev="smallest")+
geom_edge_link(n=2,edge_colour="grey66")+
geom_node_point(shape=21,fill="grey25",size=5)+
theme_graph()
ggraph(g,layout="eigen",type="laplacian",ev="largest")+
geom_edge_link(n=2,edge_colour="grey66")+
geom_node_point(shape=21,fill="grey25",size=5)+
theme_graph()
ggraph(g,layout="eigen",type="laplacian",ev="smallest")+
geom_edge_link(n=2,edge_colour="grey66")+
geom_node_point(shape=21,fill="grey25",size=5)+
theme_graph()