library(sand)
## Loading required package: igraph
## Loading required package: igraphdata
##
## Statistical Analysis of Network Data with R
## Type in C2 (+ENTER) to start with Chapter 2.
C4
## SAND Chapter 4, press N (+ENTER) to run the next code chunk
N
## <<< 4.1
## library(sand)
## data(karate)
## hist(degree(karate), col="lightblue", xlim=c(0,50),
## xlab="Vertex Degree", ylab="Frequency", main="")
N
## <<< 4.2
## hist(graph.strength(karate), col="pink",
## xlab="Vertex Strength", ylab="Frequency", main="")
N
## <<< 4.3
## library(igraphdata)
## data(yeast)
N
## <<< 4.4
## ecount(yeast)
## >>>
## [1] 11855
N
## <<< 4.5
## vcount(yeast)
## >>>
## [1] 2617
N
## <<< 4.6
## d.yeast <- degree(yeast)
## hist(d.yeast,col="blue",
## xlab="Degree", ylab="Frequency",
## main="Degree Distribution")
N
## <<< 4.7
## 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")
N
## <<< 4.8
## 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"))
N
## <<< 4.9
## A <- get.adjacency(karate, sparse=FALSE)
## library(network)
## g <- network::as.network.matrix(A)
## library(sna)
## sna::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")
## network: Classes for Relational Data
## Version 1.11.3 created on 2014-12-05.
## 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
##
## sna: Tools for Social Network Analysis
## Version 2.3-2 created on 2014-01-13.
## 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 object is masked from 'package:network':
##
## %c%
##
## The following objects are masked from 'package:igraph':
##
## %c%, betweenness, bonpow, closeness, degree, dyad.census,
## evcent, hierarchy, is.connected, neighborhood, triad.census
## 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
N
## <<< 4.10
## l <- layout.kamada.kawai(aidsblog)
## 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))
N
## <<< 4.11
## eb <- edge.betweenness(karate)
## E(karate)[order(eb, decreasing=T)[1:3]]
## >>>
## Edge sequence:
##
## [53] John A -- Actor 20
## [14] Actor 20 -- Mr Hi
## [16] Actor 32 -- Mr Hi
N
## <<< 4.12
## table(sapply(cliques(karate), length))
N
## <<< 4.13
## cliques(karate)[sapply(cliques(karate), length) == 5]
## >>>
## [[1]]
## [1] 1 2 3 4 8
##
## [[2]]
## [1] 1 2 3 4 14
N
## <<< 4.14
## table(sapply(maximal.cliques(karate), length))
N
## <<< 4.15
## clique.number(yeast)
## >>>
## [1] 23
N
## <<< 4.16
## cores <- graph.coreness(karate)
## sna::gplot.target(g, cores, circ.lab = FALSE,
## circ.col="skyblue", usearrows = FALSE,
## vertex.col=cores, edge.col="darkgray")
## detach("package:network")
## detach("package:sna")
## 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
N
## <<< 4.17
## aidsblog <- simplify(aidsblog)
## dyad.census(aidsblog)
## >>>
## $mut
## [1] 3
##
## $asym
## [1] 177
##
## $null
## [1] 10405
N
## <<< 4.18
## ego.instr <- induced.subgraph(karate,
## neighborhood(karate, 1, 1)[[1]])
## ego.admin <- induced.subgraph(karate,
## neighborhood(karate, 1, 34)[[1]])
## graph.density(karate)
## graph.density(ego.instr)
## graph.density(ego.admin)
## >>>
## [1] 0.2091503
N
## <<< 4.19
## transitivity(karate)
## >>>
## [1] 0.2556818
N
## <<< 4.20
## transitivity(karate, "local", vids=c(1,34))
## >>>
## [1] 0.1500000 0.1102941
N
## <<< 4.21
## reciprocity(aidsblog, mode="default")
## reciprocity(aidsblog, mode="ratio")
## >>>
## [1] 0.01666667
N
## <<< 4.22
## is.connected(yeast)
## >>>
## [1] FALSE
N
## <<< 4.23
## comps <- decompose.graph(yeast)
## table(sapply(comps, vcount))
N
## <<< 4.24
## yeast.gc <- decompose.graph(yeast)[[1]]
N
## <<< 4.25
## average.path.length(yeast.gc)
## >>>
## [1] 5.09597
N
## <<< 4.26
## diameter(yeast.gc)
## >>>
## [1] 15
N
## <<< 4.27
## transitivity(yeast.gc)
## >>>
## [1] 0.4686663
N
## <<< 4.28
## vertex.connectivity(yeast.gc)
## edge.connectivity(yeast.gc)
## >>>
## [1] 1
N
## <<< 4.29
## yeast.cut.vertices <- articulation.points(yeast.gc)
## length(yeast.cut.vertices)
## >>>
## [1] 350
N
## <<< 4.30
## is.connected(aidsblog, mode=c("weak"))
## >>>
## [1] TRUE
N
## <<< 4.31
## is.connected(aidsblog, mode=c("strong"))
## >>>
## [1] FALSE
N
## <<< 4.32
## aidsblog.scc <- clusters(aidsblog, mode=c("strong"))
## table(aidsblog.scc$csize)
N
## <<< 4.33
## kc <- fastgreedy.community(karate)
N
## <<< 4.34
## length(kc)
## sizes(kc)
## >>>
## Community sizes
## 1 2 3
## 18 11 5
N
## <<< 4.35
## 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
N
## <<< 4.36
## plot(kc,karate)
N
## <<< 4.37
## library(ape)
## dendPlot(kc, mode="phylo")
##
## Attaching package: 'ape'
##
## The following object is masked from 'package:igraph':
##
## edges
N
## <<< 4.38
## k.lap <- graph.laplacian(karate)
## eig.anal <- eigen(k.lap)
N
## <<< 4.39
## plot(eig.anal$values, col="blue",
## ylab="Eigenvalues of Graph Laplacian")
N
## <<< 4.40
## f.vec <- eig.anal$vectors[, 33]
N
## <<< 4.41
## 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="Fiedler Vector Entry", col=f.colors)
## abline(0, 0, lwd=2, col="lightgray")
N
## <<< 4.42
## 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
N
## <<< 4.43
## yc <- fastgreedy.community(yeast.gc)
## c.m <- membership(yc)
N
## <<< 4.44
## 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
N
## <<< 4.45
## assortativity.nominal(yeast, (V(yeast)$Class=="P")+1,
## directed=FALSE)
## >>>
## [1] 0.4965229
N
## <<< 4.46
## assortativity.degree(yeast)
## >>>
## [1] 0.4610798
N
## No more code chunks in Chapter 4.
N
## No more code chunks in Chapter 4.