R包—iGraph - 码农教程

这几天收到师兄的任务，熟悉iGRaph包的使用，通过查资料，外加自己的实践，在此做个简单的学习笔记。以下例子均是在R 3.0.1版本下测试的。

1.用igraph创建图表

 g<- graph(c(1,2, 1,3, 1,4, 2,4, 3,4), directed=T) 
 > g IGRAPH D--- 4 5 --  
 > plot(g, layout=layout.fruchterman.reingold)

2.创建多种图形的图表

 > g1 <- graph.full(4) > g1 IGRAPH U--- 4 6 -- Full graph + attr: name (g/c), loops (g/x) 
 > g2 <- graph.ring(3) > g2 IGRAPH U--- 3 3 -- Ring graph + attr: name (g/c), mutual (g/x), circular (g/x) 
 > g3 = graph.lattice(c(3,4,2))#create a lattice > g3 IGRAPH U--- 24 46 -- Lattice graph + attr: name (g/c), dimvector (g/n), nei (g/n), mutual (g/x), circular (g/x) 
 >g4 = graph.tree(50, children=2)#create a tree > g4 IGRAPH D--- 50 49 -- Tree + attr: name (g/c), children (g/n), mode (g/c) 
 >plot(g4,layout=layout.fruchterman.reingold,vertex.label.dist=0.3,edge.arrow.size=0.5,vertex.color="red")

3.随机图表和优先连接的生成

 > g <- erdos.renyi.game(20, 0.2)#create a random graph and fix probability  
 > g IGRAPH U--- 20 43 -- Erdos renyi (gnp) graph + attr: name (g/c), type (g/c), loops (g/x), p (g/n) 
 > plot(g, layout=layout.fruchterman.reingold, vertex.label=NA, vertex.size=5,vertex.color="green") 

 > g <- erdos.renyi.game(20, 15, type='gnm')# Generate random graph, fixed number of arcs > g IGRAPH U--- 20 15 -- Erdos renyi (gnm) graph + attr: name (g/c), type (g/c), loops (g/x), m (g/n) 
 > plot(g, layout=layout.reingold.tilford, vertex.label=NA, vertex.size=5,vertex.color="green")

4.简单图表的算法

 g <- erdos.renyi.game(12, 0.35) > g IGRAPH U--- 12 21 -- Erdos renyi (gnp) graph + attr: name (g/c), type (g/c), loops (g/x), p (g/n) 
 > E(g)$weight <- round(runif(length(E(g))),2) * 50#Create the graph and assign random edge weights 

 > mst <- minimum.spanning.tree(g)#Compute the minimum spanning tree > mst IGRAPH U-W- 12 11 -- Erdos renyi (gnp) graph + attr: name (g/c), type (g/c), loops (g/x), p (g/n), weight (e/n)

最短路径算法：

> pa <- get.shortest.paths(g, 5, 9)[[1]]
> pa
[1] 5 3 9
> V(g)[pa]$color <- 'green'
> E(g)$color <- 'grey'
> E(g, path=pa)$color <- 'red'
> E(g, path=pa)$width <- 3
> plot(g, layout=layout.fruchterman.reingold) 

 > plot(mst, layout=layout.reingold.tilford, edge.label=E(mst)$weight) 
 > plot(g, layout=layout.fruchterman.reingold, edge.label=E(g)$weight)

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