一、igraph软件包创建图和网络
igraph 是一个独立的库，底层是 C，上层有 Python 和 R 接口，主要做图和网络方面的计算，附带绘图功能。
调试顶点的大小（参数vertex.size）和顶点标签（参数vertex.label.cex）的大小。
igraph中图的数据结构
igraph中基本的graph structure采用的是EdgeList，所以在igraph中自然而然的允许multiedge的存在，当然它也提供了Adjancency list（对某些算法，大部分算法接受的参数是edgelist）。数据结构igraph_t定义如下:
typedef struct igraph_s {
igraph_integer_t n; #图的顶点个数
igraph_bool_t directed; #有向图，无向图
igraph_vector_t from; #边的起点
igraph_vector_t to; #边的终点
igraph_vector_t oi; #尾结点下标
igraph_vector_t ii; #头结点下标
igraph_vector_t os;
igraph_vector_t is;
void *attr;
} igraph_t;
igraph中顶点和边都是从0开始编号。n是图的顶点个数，directed是有向图标识。所有边的顶点存储在from和to两个向量(igraph_vector_t)中，oi[e]对应的是编号为e的边所对应的尾结点在from中的index，同样ii[e]对应于e的头节点在to中的index（也就是是说e 可以表示为 from[oi[e]] -> to[ii[e]]）。所以from,to,oi,ii都是长度与边数相同的向量。
os和is则和oi，ii相反，表示的是从顶点到边的映射，从顶点v出发的第一条边为 from[oi[os[v]]] -> to[ii[os[v]]]，所以当os[v] == os[v + 1]时候就表示从该顶点没有出边。向量is同理。os,is都是长度为顶点数加一的向量。
操作igraph_t的一些基本API如igraph_empty, igraph_adjacent等见于文档手册。
因为采用的是edgelist的结构，所以增/减边（顶点）的操作在igraph中是相当耗费时间的。add和delete操作的时间复杂度基本上都是O(|V| + |E|)或者O(|V|)。
eg1.有weight的图
require(igraph)
d = data.frame(p1 = c('a', 'b', 'c'),
p2 = c('b', 'c', 'a'),
weight = c(1, 2, 4))
g = graph.data.frame(d, directed = TRUE) #有向图
plot(g, edge.width=E(g)$weight)
eg2. 顶点的颜色
ramp =colorRamp(c("red", "white","blue"));
#ramp(seq(0, 1, length = length(unique(label))))
panel=rgb( ramp(seq(0, 1, length = length(unique(label)))), max = 255)#设定颜色
用户可以根据color、rgb值和hsv值来设定不同的颜色
注释：R语言设定颜色的方法
library(grDevices);
ramp <- colorRamp(c("red", "white","blue"));
ramp(seq(0, 1, length = 5))
[,1] [,2] [,3]
[1,] 255.0 0.0 0.0
[2,] 255.0 127.5 127.5
[3,] 255.0 255.0 255.0
[4,] 127.5 127.5 255.0
[5,] 0.0 0.0 255.0
color<-rgb( ramp(seq(0, 1, length = 5)), max = 255)
color
[1] "#FF0000" "#FF7F7F" "#FFFFFF" "#7F7FFF" "#0000FF"
eg3. 邻接矩阵的图
library(igraph)
cells<-c(0,0,1,0,1,1,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,0,1,1,0,3,0,3,3,3,0,0,0,0,0,0,0,0,3,0,3,1,1,1,0,0,0,0,0,0,1,1
,0,3,0,0,0,0,1,0,0,0,0,0,1,1,3,1,0,0,3,0,0,0,0,0,0,0,0,0,3,1,0,3,0,0,3,1,0,3,0,0,1,1,3,1,0,0,0,0,0,3,0,3,1,1,0,0,0,0,1,3,3,0,0,3,1,3,0,0,0,0,0,0,0,0,1,3,3,0,0,3,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,3,3,3,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,1,1,0)
cells=matrix(cells,14,14,byrow=T) #创建邻接矩阵
> cells
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 0 0 1 0 1 1 0 1 0 0 0 0 0
[2,] 0 0 1 0 1 1 0 1 0 0 0 0 0
[3,] 1 1 0 3 0 3 3 3 0 0 0 0 0
[4,] 0 0 3 0 3 1 1 1 0 0 0 0 0
[5,] 1 1 0 3 0 0 0 0 1 0 0 0 0
[6,] 1 1 3 1 0 0 3 0 0 0 0 0 0
[7,] 0 0 3 1 0 3 0 0 3 1 0 3 0
[8,] 1 1 3 1 0 0 0 0 0 3 0 3 1
[9,] 0 0 0 0 1 3 3 0 0 3 1 3 0
[10,] 0 0 0 0 0 0 1 3 3 0 0 3 1
[11,] 0 0 0 0 0 0 0 0 1 0 0 0 0
[12,] 0 0 0 0 0 0 3 3 3 3 0 0 1
[13,] 0 0 0 0 0 0 0 1 0 1 0 1 0
[14,] 0 0 0 0 0 0 0 1 0 1 0 1 1
[,14]
[1,] 0
[2,] 0
[3,] 0
[4,] 0
[5,] 0
[6,] 0
[7,] 0
[8,] 1
[9,] 0
[10,] 1
[11,] 0
[12,] 1
[13,] 1
[14,] 0
myCoord<-c(1,2,7.5,5,3,6,6,8,8,11,8,10,11,13,2,1,2,4,5.5,1,6,4,9,8,14,5.5,2.2,4)
myCoord<-matrix(myCoord,14,2,byrow=F) #创建顶点坐标
> myCoord
[,1] [,2]
[1,] 1.0 2.0
[2,] 2.0 1.0
[3,] 7.5 2.0
[4,] 5.0 4.0
[5,] 3.0 5.5
[6,] 6.0 1.0
[7,] 6.0 6.0
[8,] 8.0 4.0
[9,] 8.0 9.0
[10,] 11.0 8.0
[11,] 8.0 14.0
[12,] 10.0 5.5
[13,] 11.0 2.2
[14,] 13.0 4.0
cnames=paste("e",1:14,sep="") #顶点标签
> cnames
[1] "e1" "e2" "e3" "e4" "e5" "e6" "e7" "e8" "e9" "e10" "e11" "e12"
[13] "e13" "e14"
g=graph.adjacency(cells,mode="undirected",weighted=T) #创建图
> g
IGRAPH U-W- 14 35 --
+ attr: weight (e/n)
+ edges:
[1] 1-- 3 1-- 5 1-- 6 1-- 8 2-- 3 2-- 5 2-- 6 2-- 8 3-- 4 3-- 6
[11] 3-- 7 3-- 8 4-- 5 4-- 6 4-- 7 4-- 8 5-- 9 6-- 7 6-- 9 7-- 9
[21] 7--10 7--12 8--10 8--12 8--13 8--14 9--10 9--11 9--12 10--12
[31] 10--13 10--14 12--13 12--14 13--14
plot(g,vertex.color="green",layout=myCoord,vertex.shape="square",
vertex.label=cnames,vertex.label.font=2,vertex.label.dist=-1,
vertex.label.degree=-pi/2,vertex.label.color="black",
vertex.frame.color="gray",
edge.width=E(g)$weight,edge.color="gray")
igraph创建图
三、函数应用
1.输出图中所有节点
V(g)$name
g是相应的图
2.根据节点degree输出节点
V(g)[degree(g)>3] 将图中degree大于3的节点输出
g是相应的图
3.
V(g) #返回图g的顶点
E(g) #返回图g的边
4.图形建立：
(1)
> g=graph(c(1,2,5,6,1,4),n=6,directed=T)
> plot(g)
(2)
>g=graph.formula(Alice-Bob-Cecil-Alice,Daniel-Cecil-Engene,Cecil-Gordon)
> plot(g)
(3)
graph.data.frame() #从数据框画图
graph.adjacency() #从邻接矩阵创建图
(4)
erdos.renyi.game() #根据Erdos-Renyi模型生成随机图
ba.game() #根据Barabasi-Albert模型生成scale-free图
(5)
vcount(g)/ecount(g) #返回图g的定点数、边数
is.connected(g) #图g是否连通
is.clusters(g) #图g有多少分支
(6)
设置图的属性：set/get.graph/vertex/edge() # 具体用法详见help
5.可视化
(1)plot()命令 :画普通的二维图
(2)tkplot():交互绘图命令
例：
> library(igraph)
> g=barabasi.game(100,m=1)
>id=tkplot(g,vertex.size=4,vertex.label=NA,edge.color="black",edge.arrow.size=0.7,vertex.color="red")
> coords <- tkplot.getcoords(id)
(3)rgplot:画3D图
随时随地看视频
