r:social_network_analysis_tutorial
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Table of Contents
T1.
install.packages("igraph")
Dataset
- star-wars-network-edges.csv
"source","target","weight" "C-3PO","R2-D2",17 "LUKE","R2-D2",13 "OBI-WAN","R2-D2",6 "LEIA","R2-D2",5 "HAN","R2-D2",5 "CHEWBACCA","R2-D2",3 "DODONNA","R2-D2",1 "CHEWBACCA","OBI-WAN",7 "C-3PO","CHEWBACCA",5 "CHEWBACCA","LUKE",16 "CHEWBACCA","HAN",19 "CHEWBACCA","LEIA",11 "CHEWBACCA","DARTH VADER",1 "CHEWBACCA","DODONNA",1 "CAMIE","LUKE",2 "BIGGS","CAMIE",2 "BIGGS","LUKE",4 "DARTH VADER","LEIA",1 "BERU","LUKE",3 "BERU","OWEN",3 "BERU","C-3PO",2 "LUKE","OWEN",3 "C-3PO","LUKE",18 "C-3PO","OWEN",2 "C-3PO","LEIA",6 "LEIA","LUKE",17 "BERU","LEIA",1 "LUKE","OBI-WAN",19 "C-3PO","OBI-WAN",6 "LEIA","OBI-WAN",1 "MOTTI","TARKIN",2 "DARTH VADER","MOTTI",1 "DARTH VADER","TARKIN",7 "HAN","OBI-WAN",9 "HAN","LUKE",26 "GREEDO","HAN",1 "HAN","JABBA",1 "C-3PO","HAN",6 "LEIA","MOTTI",1 "LEIA","TARKIN",1 "HAN","LEIA",13 "DARTH VADER","OBI-WAN",1 "DODONNA","GOLD LEADER",1 "DODONNA","WEDGE",1 "DODONNA","LUKE",1 "GOLD LEADER","WEDGE",1 "GOLD LEADER","LUKE",1 "LUKE","WEDGE",2 "BIGGS","LEIA",1 "LEIA","RED LEADER",1 "LUKE","RED LEADER",3 "BIGGS","RED LEADER",3 "BIGGS","C-3PO",1 "C-3PO","RED LEADER",1 "RED LEADER","WEDGE",3 "GOLD LEADER","RED LEADER",1 "BIGGS","WEDGE",2 "RED LEADER","RED TEN",1 "BIGGS","GOLD LEADER",1 "LUKE","RED TEN",1
- star-wars-network-nodes.csv
"name","id" "R2-D2",0 "CHEWBACCA",1 "C-3PO",2 "LUKE",3 "DARTH VADER",4 "CAMIE",5 "BIGGS",6 "LEIA",7 "BERU",8 "OWEN",9 "OBI-WAN",10 "MOTTI",11 "TARKIN",12 "HAN",13 "GREEDO",14 "JABBA",15 "DODONNA",16 "GOLD LEADER",17 "WEDGE",18 "RED LEADER",19 "RED TEN",20 "GOLD FIVE",21
Analysis
t.e <- read.csv("http://commres.net/wiki/_export/code/r/social_network_analysis_tutorial?codeblock=0", sep = ",")
head(t.e)
t.n <- read.csv("http://commres.net/wiki/_export/code/r/social_network_analysis_tutorial?codeblock=1", sep = ",")
head(t.n)
> t.e <- read.csv("http://commres.net/wiki/_export/code/r/social_network_analysis_tutorial?codeblock=0", sep = ",")
> head(t.e)
source target weight
1 C-3PO R2-D2 17
2 LUKE R2-D2 13
3 OBI-WAN R2-D2 6
4 LEIA R2-D2 5
5 HAN R2-D2 5
6 CHEWBACCA R2-D2 3
>
> t.n <- read.csv("http://commres.net/wiki/_export/code/r/social_network_analysis_tutorial?codeblock=1", sep = ",")
> head(t.n)
name id
1 R2-D2 0
2 CHEWBACCA 1
3 C-3PO 2
4 LUKE 3
5 DARTH VADER 4
6 CAMIE 5
>
library(igraph) g <- graph_from_data_frame(d=t.e, vertices=t.n, directed=FALSE) g
> library(igraph) > g <- graph_from_data_frame(d=t.e, vertices=t.n, directed=FALSE) > g IGRAPH 9b91a04 UNW- 22 60 -- + attr: name (v/c), id (v/n), weight (e/n) + edges from 9b91a04 (vertex names): [1] R2-D2 --C-3PO R2-D2 --LUKE R2-D2 --OBI-WAN [4] R2-D2 --LEIA R2-D2 --HAN R2-D2 --CHEWBACCA [7] R2-D2 --DODONNA CHEWBACCA --OBI-WAN CHEWBACCA --C-3PO [10] CHEWBACCA --LUKE CHEWBACCA --HAN CHEWBACCA --LEIA [13] CHEWBACCA --DARTH VADER CHEWBACCA --DODONNA LUKE --CAMIE [16] CAMIE --BIGGS LUKE --BIGGS DARTH VADER--LEIA [19] LUKE --BERU BERU --OWEN C-3PO --BERU [22] LUKE --OWEN C-3PO --LUKE C-3PO --OWEN + ... omitted several edges >
IGRAPH 9b91a04 UNW- 22 60 --
+ attr: name (v/c), id (v/n), weight (e/n)
+ edges from 9b91a04 (vertex names):
Vertices = Nodes
IGRAPH ID(#)
U: Undirected
N: Named graph
W: Weighted graph
name (v/c): name = vetices(nodes) graph, character
id(v/n): id = vertices(nodes) graph, numeric
weight(e/n): weight = edge graph, numeric
V(g) # print nodes
> V(g) # print nodes + 22/22 vertices, named, from 9b91a04: [1] R2-D2 CHEWBACCA C-3PO LUKE DARTH VADER CAMIE [7] BIGGS LEIA BERU OWEN OBI-WAN MOTTI [13] TARKIN HAN GREEDO JABBA DODONNA GOLD LEADER [19] WEDGE RED LEADER RED TEN GOLD FIVE > V(g)$name # print name attributes in nodes [1] "R2-D2" "CHEWBACCA" "C-3PO" "LUKE" "DARTH VADER" [6] "CAMIE" "BIGGS" "LEIA" "BERU" "OWEN" [11] "OBI-WAN" "MOTTI" "TARKIN" "HAN" "GREEDO" [16] "JABBA" "DODONNA" "GOLD LEADER" "WEDGE" "RED LEADER" [21] "RED TEN" "GOLD FIVE" > V(g)$id # id attributes of the nodes [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 >
vertex_attr(g) # all attributes of the nodes
> vertex_attr(g) # all attributes of the nodes $name [1] "R2-D2" "CHEWBACCA" "C-3PO" "LUKE" "DARTH VADER" [6] "CAMIE" "BIGGS" "LEIA" "BERU" "OWEN" [11] "OBI-WAN" "MOTTI" "TARKIN" "HAN" "GREEDO" [16] "JABBA" "DODONNA" "GOLD LEADER" "WEDGE" "RED LEADER" [21] "RED TEN" "GOLD FIVE" $id [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 >
E(g) # print edges
> E(g) # edges + 60/60 edges from 9b91a04 (vertex names): [1] R2-D2 --C-3PO R2-D2 --LUKE R2-D2 --OBI-WAN [4] R2-D2 --LEIA R2-D2 --HAN R2-D2 --CHEWBACCA [7] R2-D2 --DODONNA CHEWBACCA --OBI-WAN CHEWBACCA --C-3PO [10] CHEWBACCA --LUKE CHEWBACCA --HAN CHEWBACCA --LEIA [13] CHEWBACCA --DARTH VADER CHEWBACCA --DODONNA LUKE --CAMIE [16] CAMIE --BIGGS LUKE --BIGGS DARTH VADER--LEIA [19] LUKE --BERU BERU --OWEN C-3PO --BERU [22] LUKE --OWEN C-3PO --LUKE C-3PO --OWEN [25] C-3PO --LEIA LUKE --LEIA LEIA --BERU [28] LUKE --OBI-WAN C-3PO --OBI-WAN LEIA --OBI-WAN + ... omitted several edges >
E(g)$weight # print weight attributes in edge
> E(g)$weight # print weight attributes in edge [1] 17 13 6 5 5 3 1 7 5 16 19 11 1 1 2 2 4 1 3 3 2 3 18 2 6 17 [27] 1 19 6 1 2 1 7 9 26 1 1 6 1 1 13 1 1 1 1 1 1 2 1 1 3 3 [53] 1 1 3 1 2 1 1 1 >
edge_attr(g) # all attributes of the edges
> edge_attr(g) # all attributes of the edges $weight [1] 17 13 6 5 5 3 1 7 5 16 19 11 1 1 2 2 4 1 3 3 2 3 18 2 6 17 [27] 1 19 6 1 2 1 7 9 26 1 1 6 1 1 13 1 1 1 1 1 1 2 1 1 3 3 [53] 1 1 3 1 2 1 1 1
g[] # adjacency matrix
> g[] # adjacency matrix 22 x 22 sparse Matrix of class "dgCMatrix" [[ suppressing 22 column names ‘R2-D2’, ‘CHEWBACCA’, ‘C-3PO’ ... ]] ## 컬럼 id 생략 R2-D2 . 3 17 13 . . . 5 . . 6 . . 5 . . 1 . . . . . CHEWBACCA 3 . 5 16 1 . . 11 . . 7 . . 19 . . 1 . . . . . C-3PO 17 5 . 18 . . 1 6 2 2 6 . . 6 . . . . . 1 . . LUKE 13 16 18 . . 2 4 17 3 3 19 . . 26 . . 1 1 2 3 1 . DARTH VADER . 1 . . . . . 1 . . 1 1 7 . . . . . . . . . CAMIE . . . 2 . . 2 . . . . . . . . . . . . . . . BIGGS . . 1 4 . 2 . 1 . . . . . . . . . 1 2 3 . . LEIA 5 11 6 17 1 . 1 . 1 . 1 1 1 13 . . . . . 1 . . BERU . . 2 3 . . . 1 . 3 . . . . . . . . . . . . OWEN . . 2 3 . . . . 3 . . . . . . . . . . . . . OBI-WAN 6 7 6 19 1 . . 1 . . . . . 9 . . . . . . . . MOTTI . . . . 1 . . 1 . . . . 2 . . . . . . . . . TARKIN . . . . 7 . . 1 . . . 2 . . . . . . . . . . HAN 5 19 6 26 . . . 13 . . 9 . . . 1 1 . . . . . . GREEDO . . . . . . . . . . . . . 1 . . . . . . . . JABBA . . . . . . . . . . . . . 1 . . . . . . . . DODONNA 1 1 . 1 . . . . . . . . . . . . . 1 1 . . . GOLD LEADER . . . 1 . . 1 . . . . . . . . . 1 . 1 1 . . WEDGE . . . 2 . . 2 . . . . . . . . . 1 1 . 3 . . RED LEADER . . 1 3 . . 3 1 . . . . . . . . . 1 3 . 1 . RED TEN . . . 1 . . . . . . . . . . . . . . . 1 . . GOLD FIVE . . . . . . . . . . . . . . . . . . . . . .
g[1,] # first row of adjacency matrix data.frame(g[1,]) # easy on eyes
> g[1,] # first row of adjacency matrix
R2-D2 CHEWBACCA C-3PO LUKE DARTH VADER CAMIE BIGGS LEIA
0 3 17 13 0 0 0 5
BERU OWEN OBI-WAN MOTTI TARKIN HAN GREEDO JABBA
0 0 6 0 0 5 0 0
DODONNA GOLD LEADER WEDGE RED LEADER RED TEN GOLD FIVE
1 0 0 0 0 0
>
> data.frame(g[1,])
g.1...
R2-D2 0
CHEWBACCA 3
C-3PO 17
LUKE 13
DARTH VADER 0
CAMIE 0
BIGGS 0
LEIA 5
BERU 0
OWEN 0
OBI-WAN 6
MOTTI 0
TARKIN 0
HAN 5
GREEDO 0
JABBA 0
DODONNA 1
GOLD LEADER 0
WEDGE 0
RED LEADER 0
RED TEN 0
GOLD FIVE 0
Vis
plot(g)
par(mar=c(0,0,0,0)) plot(g)
par(mar=c(0,0,0,0))
plot(g,
vertex.color = "grey", # change color of nodes
vertex.label.color = "black", # change color of labels
vertex.label.cex = .75, # change size of labels to 75% of original size
edge.curved=.15, # add a 15% curve to the edges
edge.color="grey20") # change edge color to grey
strength(g) as.data.frame(strength(g))
> strength(g)
R2-D2 CHEWBACCA C-3PO LUKE DARTH VADER CAMIE BIGGS
50 63 64 129 11 4 14
LEIA BERU OWEN OBI-WAN MOTTI TARKIN HAN
59 9 8 49 4 10 80
GREEDO JABBA DODONNA GOLD LEADER WEDGE RED LEADER RED TEN
1 1 5 5 9 13 2
GOLD FIVE
0
> data.frame(strength(g))
strength.g.
R2-D2 50
CHEWBACCA 63
C-3PO 64
LUKE 129
DARTH VADER 11
CAMIE 4
BIGGS 14
LEIA 59
BERU 9
OWEN 8
OBI-WAN 49
MOTTI 4
TARKIN 10
HAN 80
GREEDO 1
JABBA 1
DODONNA 5
GOLD LEADER 5
WEDGE 9
RED LEADER 13
RED TEN 2
GOLD FIVE 0
>
V(g)$size <- strength(g) par(mar=c(0,0,0,0)) plot(g)
# taking the log to improve it V(g)$size <- log(strength(g)) * 4 + 3 par(mar=c(0,0,0,0)) plot(g)
plot(g,
vertex.color = "grey", # change color of nodes
vertex.label.color = "black", # change color of labels
vertex.label.cex = .75, # change size of labels to 75% of original size
edge.curved=.25, # add a 25% curve to the edges
edge.color="grey20") # change edge color to grey
V(g)$label <- ifelse( strength(g)>=10, V(g)$name, NA )
par(mar=c(0,0,0,0))
plot(g,
vertex.color = "grey", # change color of nodes
vertex.label.color = "black", # change color of labels
vertex.label.cex = .75, # change size of labels to 75% of original size
edge.curved=.25, # add a 25% curve to the edges
edge.color="grey20") # change edge color to grey
t.n
Using ifelse
t.n
> t.n
name id
1 R2-D2 0
2 CHEWBACCA 1
3 C-3PO 2
4 LUKE 3
5 DARTH VADER 4
6 CAMIE 5
7 BIGGS 6
8 LEIA 7
9 BERU 8
10 OWEN 9
11 OBI-WAN 10
12 MOTTI 11
13 TARKIN 12
14 HAN 13
15 GREEDO 14
16 JABBA 15
17 DODONNA 16
18 GOLD LEADER 17
19 WEDGE 18
20 RED LEADER 19
21 RED TEN 20
22 GOLD FIVE 21
>
t.n$name=="R2-D2"
> t.n$name=="R2-D2" [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE >
as.data.frame(t.n$name=="R2-D2")
> as.data.frame(t.n$name=="R2-D2") t.n$name == "R2-D2" 1 TRUE 2 FALSE 3 FALSE 4 FALSE 5 FALSE 6 FALSE 7 FALSE 8 FALSE 9 FALSE 10 FALSE 11 FALSE 12 FALSE 13 FALSE 14 FALSE 15 FALSE 16 FALSE 17 FALSE 18 FALSE 19 FALSE 20 FALSE 21 FALSE 22 FALSE >
tmp.a <- as.data.frame(t.n) tmp.b <- as.data.frame(t.n$name=="R2-D2") tmp <- data.frame(tmp.a, tmp.b) tmp
> tmp.a <- as.data.frame(t.n)
> tmp.b <- as.data.frame(t.n$name=="R2-D2")
> tmp <- data.frame(tmp.a, tmp.b)
> tmp
name id t.n.name.....R2.D2.
1 R2-D2 0 TRUE
2 CHEWBACCA 1 FALSE
3 C-3PO 2 FALSE
4 LUKE 3 FALSE
5 DARTH VADER 4 FALSE
6 CAMIE 5 FALSE
7 BIGGS 6 FALSE
8 LEIA 7 FALSE
9 BERU 8 FALSE
10 OWEN 9 FALSE
11 OBI-WAN 10 FALSE
12 MOTTI 11 FALSE
13 TARKIN 12 FALSE
14 HAN 13 FALSE
15 GREEDO 14 FALSE
16 JABBA 15 FALSE
17 DODONNA 16 FALSE
18 GOLD LEADER 17 FALSE
19 WEDGE 18 FALSE
20 RED LEADER 19 FALSE
21 RED TEN 20 FALSE
22 GOLD FIVE 21 FALSE
ifelse(t.n$name=="R2-D2", "yes", "no")
> ifelse(t.n$name=="R2-D2", "yes", "no") [1] "yes" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" [13] "no" "no" "no" "no" "no" "no" "no" "no" "no" "no" >
ifelse(grepl("R", t.n$name), "yes", "no") # grep function
> ifelse(grepl("R", t.n$name), "yes", "no")
[1] "yes" "no" "no" "no" "yes" "no" "no" "no" "yes" "no" "no" "no"
[13] "yes" "no" "yes" "no" "no" "yes" "no" "yes" "yes" "no"
>
tmp.a <- data.frame(t.n$name)
tmp.b <- data.frame(ifelse(grepl("R", t.n$name), "yes", "no"))
data.frame(tmp.a, tmp.b)
> tmp.a <- data.frame(t.n$name)
> tmp.b <- data.frame(ifelse(grepl("R", t.n$name), "yes", "no"))
> data.frame(tmp.a, tmp.b)
t.n.name ifelse.grepl..R...t.n.name....yes....no..
1 R2-D2 yes
2 CHEWBACCA no
3 C-3PO no
4 LUKE no
5 DARTH VADER yes
6 CAMIE no
7 BIGGS no
8 LEIA no
9 BERU yes
10 OWEN no
11 OBI-WAN no
12 MOTTI no
13 TARKIN yes
14 HAN no
15 GREEDO yes
16 JABBA no
17 DODONNA no
18 GOLD LEADER yes
19 WEDGE no
20 RED LEADER yes
21 RED TEN yes
22 GOLD FIVE no
>
Coloring nodes
dark_side <- c("DARTH VADER", "MOTTI", "TARKIN")
light_side <- c("R2-D2", "CHEWBACCA", "C-3PO", "LUKE", "CAMIE", "BIGGS",
"LEIA", "BERU", "OWEN", "OBI-WAN", "HAN", "DODONNA",
"GOLD LEADER", "WEDGE", "RED LEADER", "RED TEN", "GOLD FIVE")
other <- c("GREEDO", "JABBA")
# node we'll create a new color variable as a node property
V(g)$color <- NA
V(g)$color[V(g)$name %in% dark_side] <- "red"
V(g)$color[V(g)$name %in% light_side] <- "gold"
V(g)$color[V(g)$name %in% other] <- "grey20"
vertex_attr(g)
> dark_side <- c("DARTH VADER", "MOTTI", "TARKIN")
> light_side <- c("R2-D2", "CHEWBACCA", "C-3PO", "LUKE", "CAMIE", "BIGGS",
+ "LEIA", "BERU", "OWEN", "OBI-WAN", "HAN", "DODONNA",
+ "GOLD LEADER", "WEDGE", "RED LEADER", "RED TEN", "GOLD FIVE")
> other <- c("GREEDO", "JABBA")
> # node we'll create a new color variable as a node property
> V(g)$color <- NA
> V(g)$color[V(g)$name %in% dark_side] <- "red"
> V(g)$color[V(g)$name %in% light_side] <- "gold"
> V(g)$color[V(g)$name %in% other] <- "grey20"
> vertex_attr(g)
$name
[1] "R2-D2" "CHEWBACCA" "C-3PO" "LUKE" "DARTH VADER"
[6] "CAMIE" "BIGGS" "LEIA" "BERU" "OWEN"
[11] "OBI-WAN" "MOTTI" "TARKIN" "HAN" "GREEDO"
[16] "JABBA" "DODONNA" "GOLD LEADER" "WEDGE" "RED LEADER"
[21] "RED TEN" "GOLD FIVE"
$id
[1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
$size
[1] 18.648092 19.572539 19.635532 22.439250 12.591581 8.545177 13.556229
[8] 19.310150 11.788898 11.317766 18.567281 8.545177 12.210340 20.528107
[15] 3.000000 3.000000 9.437752 9.437752 11.788898 13.259797 5.772589
[22] -Inf
$label
[1] "R2-D2" "CHEWBACCA" "C-3PO" "LUKE" "DARTH VADER"
[6] NA "BIGGS" "LEIA" NA NA
[11] "OBI-WAN" NA "TARKIN" "HAN" NA
[16] NA NA NA NA "RED LEADER"
[21] NA NA
$color
[1] "gold" "gold" "gold" "gold" "red" "gold" "gold" "gold"
[9] "gold" "gold" "gold" "red" "red" "gold" "grey20" "grey20"
[17] "gold" "gold" "gold" "gold" "gold" "gold"
>
par(mar=c(0,0,0,0)) plot(g)
%in%
# what does %in% do? 1 %in% c(1,2,3,4) 1 %in% c(2,3,4)
> 1 %in% c(1,2,3,4) [1] TRUE > 1 %in% c(2,3,4) [1] FALSE >
dark_side <- c("DARTH VADER", "MOTTI", "TARKIN")
light_side <- c("R2-D2", "CHEWBACCA", "C-3PO", "LUKE", "CAMIE", "BIGGS",
"LEIA", "BERU", "OWEN", "OBI-WAN", "HAN", "DODONNA",
"GOLD LEADER", "WEDGE", "RED LEADER", "RED TEN", "GOLD FIVE")
other <- c("GREEDO", "JABBA")
# node we'll create a new color variable as a node property
V(g)$color <- NA # V(g)에 color라는 컬럼을 만든다
V(g)$color[V(g)$name %in% dark_side] <- "red"
# V(g)$name이 dark_side에 있는 이름이면 color 컬럼에 "red"를 적는다.
V(g)$color[V(g)$name %in% light_side] <- "gold"
V(g)$color[V(g)$name %in% other] <- "grey20"
vertex_attr(g)
par(mar=c(0,0,0,0))
plot(g)
legend(x=.75, y=.75, legend=c("Dark side", "Light side", "Other"),
pch=21, pt.bg=c("red", "gold", "grey20"), pt.cex=2, bty="n")
Edges
E(g)$width <- log(E(g)$weight) + 1.5 # create width column in the graph, g edge_attr(g)
> E(g)$width <- log(E(g)$weight) + 1.5 # create width column in the graph, g > edge_attr(g) $weight [1] 17 13 6 5 5 3 1 7 5 16 19 11 1 1 2 2 [17] 4 1 3 3 2 3 18 2 6 17 1 19 6 1 2 1 [33] 7 9 26 1 1 6 1 1 13 1 1 1 1 1 1 2 [49] 1 1 3 3 1 1 3 1 2 1 1 1 $width [1] 4.333213 4.064949 3.291759 3.109438 3.109438 [6] 2.598612 1.500000 3.445910 3.109438 4.272589 [11] 4.444439 3.897895 1.500000 1.500000 2.193147 [16] 2.193147 2.886294 1.500000 2.598612 2.598612 [21] 2.193147 2.598612 4.390372 2.193147 3.291759 [26] 4.333213 1.500000 4.444439 3.291759 1.500000 [31] 2.193147 1.500000 3.445910 3.697225 4.758097 [36] 1.500000 1.500000 3.291759 1.500000 1.500000 [41] 4.064949 1.500000 1.500000 1.500000 1.500000 [46] 1.500000 1.500000 2.193147 1.500000 1.500000 [51] 2.598612 2.598612 1.500000 1.500000 2.598612 [56] 1.500000 2.193147 1.500000 1.500000 1.500000
par(mar=c(0,0,0,0)) plot(g)
par(mfrow=c(2, 3), mar=c(0,0,1,0)) plot(g, layout=layout_randomly, main="Random") plot(g, layout=layout_in_circle, main="Circle") plot(g, layout=layout_as_star, main="Star") plot(g, layout=layout_as_tree, main="Tree") plot(g, layout=layout_on_grid, main="Grid") plot(g, layout=layout_with_fr, main="Force-directed") par(mfrow=c(1, 1), mar=c(0,0,0,0)) # set it back to the previous setting
par(mfrow=c(1,2)) set.seed(777) fr <- layout_with_fr(g, niter=1000) par(mar=c(0,0,0,0)); plot(g, layout=fr) set.seed(666) fr <- layout_with_fr(g, niter=1000) par(mar=c(0,0,0,0)); plot(g, layout=fr)
Measurements
degree(g, mode = "all") closeness(g, mode = "all", weights = NA, normalized = T) betweenness(g, directed = F, weights = NA, normalized = T)
> degree(g, mode = "all")
R2-D2 CHEWBACCA C-3PO LUKE DARTH VADER CAMIE BIGGS
7 8 10 15 5 2 7
LEIA BERU OWEN OBI-WAN MOTTI TARKIN HAN
12 4 3 7 3 3 8
GREEDO JABBA DODONNA GOLD LEADER WEDGE RED LEADER RED TEN
1 1 5 5 5 7 2
GOLD FIVE
0
> closeness(g, mode = "all", weights = NA, normalized = T)
R2-D2 CHEWBACCA C-3PO LUKE DARTH VADER CAMIE BIGGS
0.38181818 0.38888889 0.40384615 0.44680851 0.32812500 0.32307692 0.36842105
LEIA BERU OWEN OBI-WAN MOTTI TARKIN HAN
0.42000000 0.35000000 0.32812500 0.38181818 0.31343284 0.31343284 0.38888889
GREEDO JABBA DODONNA GOLD LEADER WEDGE RED LEADER RED TEN
0.28767123 0.28767123 0.34426230 0.33870968 0.33870968 0.36842105 0.32307692
GOLD FIVE
0.04545455
Warning message:
In closeness(g, mode = "all", weights = NA, normalized = T) :
At centrality.c:2784 :closeness centrality is not well-defined for disconnected graphs
> betweenness(g, directed = F, weights = NA, normalized = T)
R2-D2 CHEWBACCA C-3PO LUKE DARTH VADER CAMIE BIGGS
0.011904762 0.035487528 0.057596372 0.297278912 0.011904762 0.000000000 0.032142857
LEIA BERU OWEN OBI-WAN MOTTI TARKIN HAN
0.217120181 0.005442177 0.000000000 0.014852608 0.000000000 0.000000000 0.176190476
GREEDO JABBA DODONNA GOLD LEADER WEDGE RED LEADER RED TEN
0.000000000 0.000000000 0.011111111 0.003174603 0.003174603 0.032142857 0.000000000
GOLD FIVE
0.000000000
>
g.c.deg <- degree(g, mode = "all") g.c.clo <- closeness(g, mode = "all", weights = NA, normalized = T) g.c.bet <- betweenness(g, directed = F, weights = NA, normalized = T) tmp <- data.frame(g.c.deg, g.c.clo, g.c.bet) tmp[order(-g.c.clo, -g.c.bet),]
> g.c.deg <- degree(g, mode = "all")
> g.c.clo <- closeness(g, mode = "all", weights = NA, normalized = T)
Warning message:
In closeness(g, mode = "all", weights = NA, normalized = T) :
At centrality.c:2784 :closeness centrality is not well-defined for disconnected graphs
> g.c.bet <- betweenness(g, directed = F, weights = NA, normalized = T)
> tmp <- data.frame(g.c.deg, g.c.clo, g.c.bet)
>
> tmp
g.c.deg g.c.clo g.c.bet
R2-D2 7 0.38181818 0.011904762
CHEWBACCA 8 0.38888889 0.035487528
C-3PO 10 0.40384615 0.057596372
LUKE 15 0.44680851 0.297278912
DARTH VADER 5 0.32812500 0.011904762
CAMIE 2 0.32307692 0.000000000
BIGGS 7 0.36842105 0.032142857
LEIA 12 0.42000000 0.217120181
BERU 4 0.35000000 0.005442177
OWEN 3 0.32812500 0.000000000
OBI-WAN 7 0.38181818 0.014852608
MOTTI 3 0.31343284 0.000000000
TARKIN 3 0.31343284 0.000000000
HAN 8 0.38888889 0.176190476
GREEDO 1 0.28767123 0.000000000
JABBA 1 0.28767123 0.000000000
DODONNA 5 0.34426230 0.011111111
GOLD LEADER 5 0.33870968 0.003174603
WEDGE 5 0.33870968 0.003174603
RED LEADER 7 0.36842105 0.032142857
RED TEN 2 0.32307692 0.000000000
GOLD FIVE 0 0.04545455 0.000000000
>
g.c.bac <- distances(g, v=V(g)["R2-D2"], to=V(g), weights=NA) g.c.bac
> g.c.bac <- distances(g, v=V(g)["R2-D2"], to=V(g), weights=NA)
> g.c.bac
R2-D2 CHEWBACCA C-3PO LUKE DARTH VADER CAMIE BIGGS LEIA BERU OWEN OBI-WAN MOTTI
R2-D2 0 1 1 1 2 2 2 1 2 2 1 2
TARKIN HAN GREEDO JABBA DODONNA GOLD LEADER WEDGE RED LEADER RED TEN GOLD FIVE
R2-D2 2 1 2 2 1 2 2 2 2 Inf
r/social_network_analysis_tutorial.1574832636.txt.gz · Last modified: by hkimscil