Learn-igraph-More about igraph
0. 说明
我们接着讲更多关于对igraph对象的操作，参考Statistical Network Analysis with igraph第一章。
1. 创建igraph 对象
使用管道
library(igraph)
library(igraphdata)
library(magrittr)
library(tidyverse)
library(ggraph)
library(ggnetwork)
# Notable graphs
# make_graph can create some notable graphs. The name of the graph (case insensitive), a character scalar must be suppliced as the edges argument, and other arguments are ignored. (A warning is given is they are specified.)
# eg.
# Cubical
# The Platonic graph of the cube. A convex regular polyhedron with 8 vertices and 12 edges.
g <- make_graph("Cubical") %>%
set_vertex_attr("name",value = LETTERS[1:4])
g %>%
add_layout_(with_fr()) %>%
plot()
补充：更为实际的案例中，需要使用数据集来创建图。igraph作者提供了一些根据数据集创建好的igraph对象：
library(igraphdata)
### data:Loads specified data sets, or list the available data sets.
data(package="igraphdata")
# Data sets in package ‘igraphdata’:
#
# Koenigsberg Bridges of Koenigsberg from Euler's times
# UKfaculty Friendship network of a UK university faculty
# USairports US airport network, 2010 December
# enron Enron Email Network
# foodwebs A collection of food webs
# immuno Immunoglobulin interaction network
# karate Zachary's karate club network
# kite Krackhardt's kite
# macaque Visuotactile brain areas and connections
# rfid Hospital encounter network data
# yeast Yeast protein interaction network2. 使用iraph对象查看边和点的信息
### already
data("macaque")
macaque## IGRAPH f7130f3 DN-- 45 463 --
## + attr: Citation (g/c), Author (g/c), shape (v/c), name (v/c)
## + edges from f7130f3 (vertex names):
## [1] V1 ->V2 V1 ->V3 V1 ->V3A V1 ->V4 V1 ->V4t V1 ->MT
## [7] V1 ->PO V1 ->PIP V2 ->V1 V2 ->V3 V2 ->V3A V2 ->V4
## [13] V2 ->V4t V2 ->VOT V2 ->VP V2 ->MT V2 ->MSTd/p V2 ->MSTl
## [19] V2 ->PO V2 ->PIP V2 ->VIP V2 ->FST V2 ->FEF V3 ->V1
## [25] V3 ->V2 V3 ->V3A V3 ->V4 V3 ->V4t V3 ->MT V3 ->MSTd/p
## [31] V3 ->PO V3 ->LIP V3 ->PIP V3 ->VIP V3 ->FST V3 ->TF
## [37] V3 ->FEF V3A->V1 V3A->V2 V3A->V3 V3A->V4 V3A->VP
## [43] V3A->MT V3A->MSTd/p V3A->MSTl V3A->PO V3A->LIP V3A->DP
## + ... omitted several edges原作者是这么解释的：
This is the standard way of showing (printing) an igraph graph object on
the screen. The top line of the output declares that the object is an igraph
graph, and also lists its most important properties. A four-character long
code is printed first:
‘D/U’ The first character is either ‘D’ or ‘U’ and encodes whether the graph
is directed or undireted.
‘N’ The second letter is ‘N’ for named graphs (see Section 1.2.5). A dash
here means that the graph is not named.
‘W’ The third letter is ‘W’ if the graph is weighted (in other words, if the
graph is a valued graph, Section 2.4). Unweighted graphs have a dash in
this position.
‘B’ Finally, the fourth is ‘B’ if the graph is bipartite (two-mode, Section ??).
For unipartite (one-mode) graphs a dash is printed here.
This notation might seem quite dense at first, but it is easy to get used to and
conveys much information in a small space. Then two numbers are printed,
these are the number of vertices and the number of edges in the graph, 45
and 463 in our case. At the end of the line the name of the graph is printed,
if there is any. The next line(s) list attributes, meta-data that belong to the
vertices, edges or the graph itself. Finally, the edges of the graph are listed.
Except for very small graphs, this list is truncated, so that it fits to the screen.一些基本量的展示，之前讲过，此外，还有更多关于边的操作：
###|V|
gorder(macaque)
###[1] 45
###|E|
gsize(macaque)
###[1] 463
V(macaque)
# + 45/45 vertices, named, from f7130f3:
# [1] V1 V2 V3 V3A V4 V4t VOT VP MT MSTd/p MSTl
# [12] PO LIP PIP VIP DP 7a FST PITd PITv CITd CITv
# [23] AITd AITv STPp STPa TF TH FEF 46 3a 3b 1
# [34] 2 5 Ri SII 7b 4 6 SMA Ig Id 35
# [45] 36
E(macaque)
# + 463/463 edges from f7130f3 (vertex names):
# [1] V1 ->V2 V1 ->V3 V1 ->V3A V1 ->V4 V1 ->V4t V1 ->MT
# [7] V1 ->PO V1 ->PIP V2 ->V1 V2 ->V3 V2 ->V3A V2 ->V4
# [13] V2 ->V4t V2 ->VOT V2 ->VP V2 ->MT V2 ->MSTd/p V2 ->MSTl
# [19] V2 ->PO V2 ->PIP V2 ->VIP V2 ->FST V2 ->FEF V3 ->V1
# [25] V3 ->V2 V3 ->V3A V3 ->V4 V3 ->V4t V3 ->MT V3 ->MSTd/p
# [31] V3 ->PO V3 ->LIP V3 ->PIP V3 ->VIP V3 ->FST V3 ->TF
# [37] V3 ->FEF V3A->V1 V3A->V2 V3A->V3 V3A->V4 V3A->VP
# [43] V3A->MT V3A->MSTd/p V3A->MSTl V3A->PO V3A->LIP V3A->DP
# [49] V3A->FST V3A->FEF V4 ->V1 V4 ->V2 V4 ->V3 V4 ->V3A
# [55] V4 ->V4t V4 ->VOT V4 ->VP V4 ->MT V4 ->LIP V4 ->PIP
# + ... omitted several edges
macaque %>% ends("V1|V2")
#
# [,1] [,2]
# [1,] "V1" "V2"
macaque %>% tail_of("V1|V2")
# + 1/45 vertex, named, from f7130f3:
# [1] V1
macaque %>% head_of("V1|V2")
# + 1/45 vertex, named, from f7130f3:
# [1] V2
macaque %>% neighbors("V1",mode = "out")
# + 8/45 vertices, named, from f7130f3:
# [1] V2 V3 V3A V4 V4t MT PO PIP
macaque %>% neighbors("V1",mode = "in")
# + 8/45 vertices, named, from f7130f3:
# [1] V2 V3 V3A V4 V4t MT PO PIP
E(macaque)[.from("V1")]3. 子图
创建子图
V(macaque)["V1","V2",.nei("V1"),.nei("V2")] %>%
induced_subgraph(graph = macaque) %>%
summary()## IGRAPH cb88d15 DN-- 16 156 --
## + attr: Citation (g/c), Author (g/c), shape (v/c), name (v/c)连通
is_connected(macaque,mode = "weak")## [1] TRUEis_connected(macaque,mode = "strong")## [1] TRUE边和点的筛选：
V(macaque)[1:4]
# + 4/45 vertices, named, from f7130f3:
# [1] V1 V2 V3 V3A
V(macaque)[c("V1","V2","V3","V3A")]
# + 4/45 vertices, named, from f7130f3:
# [1] V1 V2 V3 V3建立边或者点的索引向量：
E(macaque)[1:10] %>% as_ids()
# [1] "V1|V2" "V1|V3" "V1|V3A" "V1|V4" "V1|V4t" "V1|MT" "V1|PO" "V1|PIP"
# [9] "V2|V1" "V2|V3"
V(macaque)[1:10] %>% as_ids()
# [1] "V1" "V2" "V3" "V3A" "V4" "V4t" "VOT" "VP"
# [9] "MT" "MSTd/p"类似于算数操作，关于点的操作汇总：
data("kite")
V(kite)
# + 10/10 vertices, named, from 6b7ddad:
# [1] A B C D E F G H I J
V(kite)[1:3,7:10]
# + 7/10 vertices, named, from 6b7ddad:
# [1] A B C G H I J
V(kite)[degree(kite) < 2]
# + 1/10 vertex, named, from 6b7ddad:
# [1] J
V(kite)[.nei("D")]
# + 6/10 vertices, named, from 6b7ddad:
# [1] A B C E F G
V(kite)[.innei("D")]
# + 6/10 vertices, named, from 6b7ddad:
# [1] A B C E F G
V(kite)[.outnei("D")]
# + 6/10 vertices, named, from 6b7ddad:
# [1] A B C E F G
V(kite)[.inc("A|D")]
# + 2/10 vertices, named, from 6b7ddad:
# [1] A D
c(V(kite)["A"],V(kite)["D"])
# + 2/10 vertices, named, from 6b7ddad:
# [1] A D
rev(V(kite))
# + 10/10 vertices, named, from 6b7ddad:
# [1] J I H G F E D C B A
unique(V(kite)["A","A","C","C"])
# + 2/10 vertices, named, from 6b7ddad:
# [1] A C
### Set operation
union(V(kite)[1:5],v(kite)[6:10])
# + 2/10 vertices, named, from 6b7ddad:
# [1] A C
intersection(V(kite)[1:7],V(kite)[5:10])
# + 3/10 vertices, named, from 6b7ddad:
# [1] E F G
difference(V(kite),V(kite)[1:5])
# + 5/10 vertices, named, from 6b7ddad:
# [1] F G H I J
E(kite)
# + 18/18 edges from 6b7ddad (vertex names):
# [1] A--B A--C A--D A--F B--D B--E B--G C--D C--F D--E D--F D--G E--G F--G F--H G--H
# [17] H--I I--J
E(kite,path = c("A","D","C"))
# + 2/18 edges from 6b7ddad (vertex names):
# [1] A--D C--D
E(kite)[ V(kite)[1:2] %--% V(kite)[3:4] ]
# + 3/18 edges from 6b7ddad (vertex names):
# [1] A--C A--D B--D
E(kite)[1:3,7:10]
# + 7/18 edges from 6b7ddad (vertex names):
# [1] A--B A--C A--D B--G C--D C--F D--E
E(kite)[seq_len(gsize(kite))[seq_len(gsize(kite)) %%2 == 0]]
# + 9/18 edges from 6b7ddad (vertex names):
# [1] A--C A--F B--E C--D D--E D--G F--G G--H I--J
E(kite)[seq_len(gsize(kite)) %%2 == 0]
# + 9/18 edges from 6b7ddad (vertex names):
# [1] A--C A--F B--E C--D D--E D--G F--G G--H I--J
E(kite)[seq_len(gsize(kite)) %%2]
# + 9/18 edges from 6b7ddad (vertex names):
# [1] A--B A--B A--B A--B A--B A--B A--B A--B A--B
E(kite)[.inc("D")]
# + 6/18 edges from 6b7ddad (vertex names):
# [1] A--D B--D C--D D--E D--F D--G
E(macaque)[.from("V1")]
# + 8/463 edges from f7130f3 (vertex names):
# [1] V1->V2 V1->V3 V1->V3A V1->V4 V1->V4t V1->MT V1->PO V1->PIP
E(macaque)[.to("V1")]
# + 8/463 edges from f7130f3 (vertex names):
# [1] V2 ->V1 V3 ->V1 V3A->V1 V4 ->V1 V4t->V1 MT ->V1 PO ->V1 PIP->V1
### The remains are same as Vertices operations