r - How to make grouped layout in igraph?

Question

In igraph, after applying a modularization algorithm to find graph communites, i would like to draw a network layout which clearly makes visible the distinct communities and their connections. Something like "group attributes layout" in Cytoscape: i want to show the members of each group/community close to each other, and keep some distance between groups/communities. I couldn't find any function in igraph providing this feature out of the box. While posting this question i have already found out a simple d.i.y solution, i going to post it as an answer. But i am wondering if there is any better possibility, or more elaborated solution?

Answer 1

受 Antoine 建议的启发，我创建了这个函数：

edge.weights <- function(community, network, weight.within = 100, weight.between = 1) {
bridges <- crossing(communities = community, graph = network)
weights <- ifelse(test = bridges, yes = weight.between, no = weight.within)
return(weights) 
}

该功能也是如此；只需将您的社区对象放在社区插槽中，您的图形在网络中。我会离开weight.between = 1并调整weight.within值。

然后将权重转移到weight节点中的槽：

E(graph)$weight <- edge.weights(community, graph)

最后使用一种使用权重的布局算法，例如（ inlayout_with_fr的新名称）。fruchterman.reingoldigraph 1.0.1

我以 Zachary 的空手道俱乐部网络为例。

library(igraph)
library(igraphdata)
#I load the network
data(karate)
#for reproducible purposes
set.seed(23548723)
karateLayout <- layout_with_fr(karate)
par(mar = c(0,0,2,0))
plot(karate, vertex.size = 10, vertex.color = "steelblue4", edge.width = 1, 
vertex.label = NA, edge.color = "darkgrey", layout = karateLayout,
main = "Zachary's karate club network" )

我通过使用以下功能对模块化进行多级优化来检测社区cluster_louvain：

Communitykarate <- cluster_louvain(karate)

接下来是个人偏好而不是默认值：

prettyColors <- c("turquoise4", "azure4", "olivedrab","deeppink4")
communityColors <- prettyColors[membership(Communitykarate)]

使用颜色突出显示社区的图表是：

plot(x = Communitykarate, y = karate, edge.width = 1, vertex.size = 10, 
vertex.label = NA, mark.groups = NULL, layout = karateLayout, col = communityColors,
main = "Communities in Zachary's karate club network",
edge.color = c("darkgrey","tomato2")crossing(Communitykarate, karate) + 1])

现在，这个问题存在的意义。

E(karate)$weight <- edge.weights(Communitykarate, karate)
# I use the original layout as a base for the new one
karateLayoutA <- layout_with_fr(karate, karateLayout)
# the graph with the nodes grouped
plot(x = Communitykarate, y = karate, edge.width = 1, vertex.size = 10, 
mark.groups = NULL, layout = karateLayoutA, vertex.label = NA, col = communityColors, 
c("darkgrey","tomato2")[crossing(Communitykarate, karate) + 1],
main = "Communities in Zachary's karate club network (grouped)")

如果您尝试增加体重，您将拥有：

E(karate)$weight <- edge.weights(Communitykarate, karate, weight.within = 1000)
karateLayoutB <- layout_with_fr(karate, karateLayout)
plot(x = Communitykarate, y = karate, edge.width = 1, vertex.size = 10,
 mark.groups = NULL, layout = karateLayoutB, vertex.label = NA, col = communityColors, 
c("darkgrey","tomato2")[crossing(Communitykarate, karate) + 1],
main = "Communities in Zachary's karate club network (grouped)")

Answer 2

为了扩展 Gabor 的建议，我创建了这个函数：

weight.community=function(row,membership,weigth.within,weight.between){
if(as.numeric(membership[which(names(membership)==row[1])])==as.numeric(membership[which(names(membership)==row[2])])){
weight=weigth.within
}else{
weight=weight.between
}
return(weight)
}

只需将其应用于图形边矩阵的行（由get.edgelist(your_graph))设置新边权重（成员资格是任何社区检测算法结果的成员资格向量）：

E(g)$weight=apply(get.edgelist(g),1,weight.community,membership,10,1)

然后，只需使用接受边缘权重的布局算法，例如 Gabor 建议的 fruchterman.reingold。您可以调整权重参数以获得所需的图形。例如：

E(g)$weight=apply(get.edgelist(g),1,weight.community,membership,10,1)
g$layout=layout.fruchterman.reingold(g,weights=E(g)$weight)
plot(g)

E(g)$weight=apply(get.edgelist(g),1,weight.community,membership,1000,1)
g$layout=layout.fruchterman.reingold(g,weights=E(g)$weight)
plot(g)

注 1：边缘的透明度/颜色是我的图表的其他参数。我按社区对节点进行了着色，以表明它确实有效。

注意 2：确保使用membership(comm)和 不，社区检测算法的结果在comm$membership哪里（例如， ）。原因是在第一种情况下，你得到一个带有名字的数字向量（我们想要的），而在第二种情况下，你得到一个没有名字的同一个向量。commcomm=leading.eigenvector.community(g)

要获得多个社区检测算法的共识，请参阅此功能。

Answer 3

该函数layout.modular根据任何 igraph 社区检测方法的结果为图提供分组布局：

c <- fastgreedy.community(G)

layout.modular <- function(G,c){
nm <- length(levels(as.factor(c$membership)))
gr <- 2
while(gr^2<nm){
    gr <- gr+1
}
i <- j <- 0
for(cc in levels(as.factor(c$membership))){
    F <- delete.vertices(G,c$membership!=cc)
    F$layout <- layout.kamada.kawai(F)
    F$layout <- layout.norm(F$layout, i,i+0.5,j,j+0.5)
    G$layout[c$membership==cc,] <- F$layout
    if(i==gr){
        i <- 0
        if(j==gr){
            j <- 0
        }else{
            j <- j+1
        }
    }else{
        i <- i+1
    }
}
return(G$layout)
}

G$layout <- layout.modular(G,c)
V(G)$color <- rainbow(length(levels(as.factor(c$membership))))[c$membership]
plot(G)

Answer 4

一种解决方案是基于模块化设置图的边权重。将模块内边缘设置为较大的权重，将模块边缘设置为较小的权重。然后调用layout.fruchterman.reingold()，或任何支持边缘权重的算法。

您可能需要对实际重量值进行一些调整，因为这取决于您的图表。