library(sand)
## Loading required package: igraph
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
## Loading required package: igraphdata
##
## Statistical Analysis of Network Data with R
## Type in C2 (+ENTER) to start with Chapter 2.
library(igraph)
data(karate)
hist(degree(karate),col="lightblue",xlim=c(0,50),
xlab="Vertex Degree", ylab="Frequency", main="")
hist(graph.strength(karate),col="pink",
xlab="Vertex strength", ylab="Frequency",main="")
library(igraphdata)
data(yeast)
ecount(yeast)
## [1] 11855
vcount(yeast)
## [1] 2617
par(mfrow=c(1,2))
d.yeast=degree(yeast)
hist(d.yeast,col="blue",xlab="Degree",ylab="Frequency",main="Degree Distribution")
dd.yeast=degree.distribution(yeast)
d=1:max(d.yeast)-1
ind=(dd.yeast !=0)
plot(d[ind],dd.yeast[ind],log="xy",col="blue",xlab=c("Log-Degree"),
ylab=c("Log-Intensity"),main="Log-Log Degree Distribution")
a.nn.deg.yeast=graph.knn(yeast,V(yeast))$knn
plot(d.yeast,a.nn.deg.yeast,log="xy",col="goldenrod",
xlab=c("Log Vertex Degree"),ylab=c("Log Average Neighbor Degree"))
#install.packages("network", repos="http://cran.us.r-project.org")
#install.packages("sna", repos="http://cran.us.r-project.org")
A=get.adjacency(karate,sparse=FALSE)
library(network)
## network: Classes for Relational Data
## Version 1.16.0 created on 2019-11-30.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Martina Morris, University of Washington
## Skye Bender-deMoll, University of Washington
## For citation information, type citation("network").
## Type help("network-package") to get started.
##
## Attaching package: 'network'
## The following objects are masked from 'package:igraph':
##
## %c%, %s%, add.edges, add.vertices, delete.edges, delete.vertices,
## get.edge.attribute, get.edges, get.vertex.attribute, is.bipartite,
## is.directed, list.edge.attributes, list.vertex.attributes,
## set.edge.attribute, set.vertex.attribute
g=as.network.matrix(A)
library(sna)
## Loading required package: statnet.common
##
## Attaching package: 'statnet.common'
## The following object is masked from 'package:base':
##
## order
## sna: Tools for Social Network Analysis
## Version 2.5 created on 2019-12-09.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## For citation information, type citation("sna").
## Type help(package="sna") to get started.
##
## Attaching package: 'sna'
## The following objects are masked from 'package:igraph':
##
## betweenness, bonpow, closeness, components, degree, dyad.census,
## evcent, hierarchy, is.connected, neighborhood, triad.census
par(mfrow=c(1,2))
gplot.target(g,degree(g),main="Degree",circ.lab=FALSE,circ.col="skyblue",
usearrows=FALSE,vertex.col=c("blue",rep("red",32),"yellow"),
edge.col="darkgray")
## Warning in plot.window(...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in plot.xy(xy, type, ...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in title(...): "circ.lab"는 그래픽 매개변수가 아닙니다
gplot.target(g,closeness(g),main="Closeness",circ.lab=FALSE,circ.col="skyblue",
usearrows=FALSE,vertex.col=c("blue",rep("red",32),"yellow"),
edge.col="darkgray")
## Warning in plot.window(...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in plot.xy(xy, type, ...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in title(...): "circ.lab"는 그래픽 매개변수가 아닙니다
#install.packages("network",repos="http://cran.us.r-project.org")
#install.packages("sna",repos="http://cran.us.r-project.org")
A=get.adjacency(karate,sparse=FALSE)
library(network)
g=as.network.matrix(A)
library(sna)
par(mfrow=c(1,2))
gplot.target(g,betweenness(g), main="Betweenness", circ.lab=FALSE, circ.col="skyblue", usearrows=FALSE, vertex.col=c("blue",rep("red",32),"yellow"), edge.col="darkgrey")
## Warning in plot.window(...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in plot.xy(xy, type, ...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in title(...): "circ.lab"는 그래픽 매개변수가 아닙니다
gplot.target(g,evcent(g), main="Eigenvalue", circ.lab=FALSE, circ.col="skyblue", usearrows=FALSE, vertex.col=c("blue",rep("red",32),"yellow"), edge.col="darkgrey")
## Warning in plot.window(...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in plot.xy(xy, type, ...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in title(...): "circ.lab"는 그래픽 매개변수가 아닙니다
l=layout.kamada.kawai(aidsblog)
par(mfrow=c(1,2))
plot(aidsblog, layout=l, main="Hubs",vertex.label="",
vertex.size=10 * sqrt(hub.score(aidsblog)$vector))
plot(aidsblog, layout=l, main="Authorities", vertex.label="",
vertex.size=10 * sqrt(authority.score(aidsblog)$vector))
eb=edge.betweenness(karate)
E(karate)[order(eb,decreasing=T)[1:3]]
## + 3/78 edges from 4b458a1 (vertex names):
## [1] Actor 20--John A Mr Hi --Actor 20 Mr Hi --Actor 32
table(sapply(cliques(karate),length))
##
## 1 2 3 4 5
## 34 78 45 11 2
cliques(karate)[sapply(cliques(karate), length)==5]
## [[1]]
## + 5/34 vertices, named, from 4b458a1:
## [1] Mr Hi Actor 2 Actor 3 Actor 4 Actor 14
##
## [[2]]
## + 5/34 vertices, named, from 4b458a1:
## [1] Mr Hi Actor 2 Actor 3 Actor 4 Actor 8
table(sapply(maximal.cliques(karate),length))
##
## 2 3 4 5
## 11 21 2 2
clique.number(yeast)
## [1] 23
cores=graph.coreness(karate)
gplot.target(g, cores, circ.lab=FALSE, circ.col="skyblue",
usearrows= FALSE, vertex.col=cores, edge.col="darkgray")
## Warning in plot.window(...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in plot.xy(xy, type, ...): "circ.lab"는 그래픽 매개변수가 아닙니다
## Warning in title(...): "circ.lab"는 그래픽 매개변수가 아닙니다
detach("package:sna")
detach("package:network")
aidsblog=simplify(aidsblog)
dyad.census(aidsblog)
## $mut
## [1] 3
##
## $asym
## [1] 177
##
## $null
## [1] 10405
ego.instr=induced.subgraph(karate,neighborhood(karate, 1, 1)[[1]])
ego.admin=induced.subgraph(karate,neighborhood(karate, 1, 34)[[1]])
graph.density(karate)
## [1] 0.1390374
graph.density(ego.instr)
## [1] 0.25
graph.density(ego.admin)
## [1] 0.2091503
transitivity(karate)
## [1] 0.2556818
transitivity(karate, "local", vids=c(1,34))
## [1] 0.1500000 0.1102941
reciprocity(aidsblog,mode="default")
## [1] 0.03278689
reciprocity(aidsblog,mode="ratio")
## [1] 0.01666667
is.connected(yeast)
## [1] FALSE
comps=decompose.graph(yeast)
table(sapply(comps, vcount))
##
## 2 3 4 5 6 7 2375
## 63 13 5 6 1 3 1
yeast.gc=decompose.graph(yeast)[[1]]
average.path.length(yeast.gc)
## [1] 5.09597
diameter(yeast.gc)
## [1] 15
transitivity(yeast.gc)
## [1] 0.4686663
vertex.connectivity(yeast.gc)
## [1] 1
edge.connectivity(yeast.gc)
## [1] 1
yeast.cut.vertices=articulation.points(yeast.gc)
length(yeast.cut.vertices)
## [1] 350
kc=fastgreedy.community(karate)
length(kc)
## [1] 3
sizes(kc)
## Community sizes
## 1 2 3
## 18 11 5
membership(kc)
## Mr Hi Actor 2 Actor 3 Actor 4 Actor 5 Actor 6 Actor 7 Actor 8
## 2 2 2 2 3 3 3 2
## Actor 9 Actor 10 Actor 11 Actor 12 Actor 13 Actor 14 Actor 15 Actor 16
## 1 1 3 2 2 2 1 1
## Actor 17 Actor 18 Actor 19 Actor 20 Actor 21 Actor 22 Actor 23 Actor 24
## 3 2 1 2 1 2 1 1
## Actor 25 Actor 26 Actor 27 Actor 28 Actor 29 Actor 30 Actor 31 Actor 32
## 1 1 1 1 1 1 1 1
## Actor 33 John A
## 1 1
plot(kc,karate)
#install.packages("ape")
library(ape)
##
## Attaching package: 'ape'
## The following objects are masked from 'package:igraph':
##
## edges, mst, ring
dendPlot(kc,mode="phylo")
k.lap=graph.laplacian(karate)
eig.anal=eigen(k.lap)
plot(eig.anal$values, col="blue", ylab="Eigenvalues of Graph Laplacian")
f.vec=eig.anal$vectors[,33]
faction=get.vertex.attribute(karate,"Faction")
f.colors=as.character(length(faction))
f.colors[faction==1]="red"
f.colors[faction==2]="cyan"
plot(f.vec, pch=16, xlab="Actor Number", ylab="Fideler Vector Entry", col=f.colors)
abline(0,0, lwd=2, col="lightgray")
func.class=get.vertex.attribute(yeast.gc,"Class")
table(func.class)
## func.class
## A B C D E F G M O P R T U
## 51 98 122 238 95 171 96 278 171 248 45 240 483
yc=fastgreedy.community(yeast.gc)
c.m=membership(yc)
table(c.m,func.class,useNA = c("no"))
## func.class
## c.m A B C D E F G M O P R T U
## 1 0 0 0 1 3 7 0 6 3 110 2 35 14
## 2 0 2 2 7 1 1 1 4 39 5 0 4 27
## 3 1 9 7 18 4 8 4 20 10 23 8 74 64
## 4 25 11 10 22 72 84 81 168 14 75 16 27 121
## 5 1 7 5 14 0 4 0 2 3 6 1 34 68
## 6 1 24 1 4 1 4 0 7 0 1 0 19 16
## 7 6 18 6 76 7 9 3 7 8 5 1 7 33
## 8 8 12 67 59 1 34 0 19 60 10 7 6 73
## 9 4 1 7 7 2 10 5 3 2 0 3 0 11
## 10 0 0 0 6 0 0 0 2 0 5 0 11 1
## 11 0 9 0 10 1 3 0 0 0 0 0 2 4
## 12 0 1 3 0 0 0 0 6 10 0 0 0 2
## 13 0 1 1 2 0 1 0 0 2 0 0 16 10
## 14 1 0 4 1 0 1 0 0 4 0 1 0 11
## 15 0 1 0 0 0 2 0 2 0 0 1 0 8
## 16 0 1 2 0 0 1 0 0 10 0 0 0 0
## 17 0 0 1 3 0 0 0 2 0 0 0 2 3
## 18 0 0 0 0 3 1 0 9 0 0 1 0 1
## 19 0 1 1 1 0 0 0 0 0 0 0 0 3
## 20 0 0 0 6 0 0 0 1 0 0 0 1 2
## 21 1 0 0 0 0 0 0 0 6 0 0 1 0
## 22 0 0 0 0 0 0 0 1 0 0 0 0 8
## 23 0 0 0 0 0 0 0 4 0 0 0 0 0
## 24 0 0 0 0 0 0 2 2 0 0 0 1 0
## 25 0 0 0 0 0 0 0 5 0 0 0 0 0
## 26 0 0 1 0 0 0 0 4 0 0 1 0 1
## 27 3 0 4 0 0 1 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 6 0 0 0
## 29 0 0 0 1 0 0 0 1 0 0 3 0 0
## 30 0 0 0 0 0 0 0 0 0 2 0 0 2
## 31 0 0 0 0 0 0 0 3 0 0 0 0 0
#assortativity.nominal(yeast, #(V(yeast)$Class=="P")+1,directed=FALSE)
assortativity.degree(yeast)
## [1] 0.4610798