Question

我一直在玩R中的igraph，当我想象一个网络时我很难使用权重。我已经读过这可能不适用于每种布局类型，但应该与Fruchterman-Reingold一起使用。

我的代码和输出在下面（我尝试了两个不同版本的布局功能，我认为他们正在做同样的事情，但是为了以防万一都试过）

我希望塞西尔和鲍勃在第一节中非常接近，因为他们的关系权重很高，但这似乎不会发生。只有当我为Bob和Cecil创建额外的行（vers2）时，这似乎才会发生，但这对于我真正想要用更大的数据集做的事情来说会很痛苦。

我会发布我正在获得的图像，但我是新的堆叠溢出并没有足够的声誉点。

有什么想法吗？提前致谢。

代码：

#vers1
library(igraph)


relations <- data.frame(from=c("Bob", "Cecil", "Cecil", "David",
                               "David", "Esmeralda"),
                        to=c("Alice", "Bob", "Alice", "Alice", "Bob",
                             "Alice"),

                        weight=c(1,100,1,1,1,1)) 




graph<-graph_from_data_frame(relations, directed=F)


coords1<-layout_with_fr(graph, weights=E(graph)$weight)
coords2 <- layout.fruchterman.reingold(graph, weights=E(graph)$weight);

plot(graph,layout=coords1)
plot(graph,layout=coords2)

#vers2
library(igraph)


relations <- data.frame(from=c("Bob", "Cecil", "Cecil", "David",
                               "David", "Esmeralda",
                               "Cecil",
                               "Cecil",
                               "Cecil",
                               "Cecil",
                               "Cecil"),
                        to=c("Alice", "Bob", "Alice", "Alice", "Bob",
                             "Alice",
                             "Bob",
                             "Bob",
                             "Bob",
                             "Bob", 
                             "Bob"),

                        weight=c(1,1,1,1,1,1,1,1,1,1,1)) 




graph<-graph_from_data_frame(relations, directed=F)


coords1<-layout_with_fr(graph, weights=E(graph)$weight)
coords2 <- layout.fruchterman.reingold(graph, weights=E(graph)$weight);

plot(graph,layout=coords1)
plot(graph,layout=coords2)

Answer 1

以下是使用sna和network库的版本。我记录了你的100到3的值，使它成为一种物理上不可能的东西。但您可以使用日志值或其他内容。注意还需要将值从相似度转换为距离。

library(sna)
library(network)

# recode the desired distances to something more reasonable
# (can't phiscally have one distance 100X the others)
relations <- data.frame(from=c("Bob", "Cecil", "Cecil", "David",
                               "David", "Esmeralda"),
                        to=c("Alice", "Bob", "Alice", "Alice", "Bob",
                             "Alice"),

                        weight=c(1,3,1,1,1,1)) 

# convert to network object including edge weights
relNet<-network(relations,ignore.eval = FALSE,names.eval='weight',matrix.type='edgelist',directed=FALSE)
# valued construct adjacency matrix
adjMat<-as.matrix(relNet,attrname='weight')
# convert from distances to similarities
adjMat[adjMat!=0]<-4-adjMat[adjMat!=0]
# construct an appropriate geodesic distance matrix from the similarities
distMat<-geodist(adjMat,ignore.eval=FALSE,inf.replace = sqrt(network.size(relNet)))$gdist
# compute coords using distance matrix and kk algorithm
coords<-network.layout.kamadakawai(relNet,layout.par=list(elen=distMat))
# plot using precomputed coords
plot(relNet,displaylabels=TRUE,coord=coords,edge.label='weight',edge.lwd='weight')

R igraph可视化加权连接

1 个答案: