# Network's characteristics and subgroups
library(sna)

# Faux Magnolia High School data
library(ergm)
data(faux.magnolia.high)
summary(faux.magnolia.high)
fmh <- as.sociomatrix(faux.magnolia.high)
gplot(fmh, vertex.cex=0.5, arrowhead.cex=0.5)

round(gden(fmh),4)

[1] 9e-04

transivity

fmh2 <- fmh %*% fmh
(denom <- sum(fmh2)-sum(diag(fmh2)))
(num   <- sum(fmh2 * fmh))
round(num/denom,3)
round(gtrans(fmh),3)

> fmh2 <- fmh %*% fmh 
> (denom <- sum(fmh2)-sum(diag(fmh2))) 
[1] 3642
> (num   <- sum(fmh2 * fmh)) 
[1] 1014
> round(num/denom,3) 
[1] 0.278
> round(gtrans(fmh),3) 
[1] 0.278

reciprocity

# knoke.infor network is given in Chapter 2, Section 1
library(sna)
knoke.infor <- as.matrix(read.table("knoke-infor.txt",header=T))
rownames(knoke.infor) <- colnames(knoke.infor)
(num <- sum(knoke.infor * t(knoke.infor)))
(denom <- sum(knoke.infor))
round(num/denom,3)
round(grecip(knoke.infor, measure="edgewise"),3)

triad census

# knoke.money network
knoke.money <- as.matrix(read.table("knoke-money.txt",header=T))
rownames(knoke.money) <- colnames(knoke.money)

# dyad and triad patterns in knoke-infor and knoke-money
dyad.census(knoke.infor)
dyad.census(knoke.money)
triad.census(knoke.infor)
triad.census(knoke.money)

> triad.census(knoke.infor)
     003 012 102 021D 021U 021C 111D 111U 030T 030C 201 120D 120U 120C 210 300
[1,]   3  13  12    1   13    3   10    4    7    0  14    4    2    1  24   9
> triad.census(knoke.money)
     003 012 102 021D 021U 021C 111D 111U 030T 030C 201 120D 120U 120C 210 300
[1,]  20  48   2   10   13    7    1    1   14    0   0    3    0    1   0   0

component.dist(knoke.money)

> component.dist(knoke.money) 
$membership
 [1] 1 2 3 4 5 6 7 3 3 8

$csize
[1] 1 1 3 1 1 1 1 1

$cdist
 [1] 7 0 1 0 0 0 0 0 0 0

# Faux Magnolia High School data (Continued)
components(fmh)
fmh.comp <- component.dist(fmh)
names(fmh.comp)
table(fmh.comp$csize)
head(fmh.comp$membership,100)
grp2 <- (1:1461)[fmh.comp$membership=="2"]
str(grp2)

# Faux Magnolia High School data (Continued)
fmh.clique <- clique.census(fmh, clique.comembership="bysize")
names(fmh.clique)
str(fmh.clique$cliques)
core.list <- c(122,183,530,1143,1160) # fmh.clique$cliques[[5]]
fmh.comp$membership[core.list]
fmh[core.list,core.list]
summary(sort(geodist(fmh)$gdist[122,])[2:439])[-4]

# end