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pause <- function() {
cat("Press ENTER/RETURN/NEWLINE to continue.")
readLines(n=1)
invisible()
}
### Traditional approaches: degree, closeness, betweenness
g <- graph.formula(Andre----Beverly:Diane:Fernando:Carol,
Beverly--Andre:Diane:Garth:Ed,
Carol----Andre:Diane:Fernando,
Diane----Andre:Carol:Fernando:Garth:Ed:Beverly,
Ed-------Beverly:Diane:Garth,
Fernando-Carol:Andre:Diane:Garth:Heather,
Garth----Ed:Beverly:Diane:Fernando:Heather,
Heather--Fernando:Garth:Ike,
Ike------Heather:Jane,
Jane-----Ike )
pause()
### Hand-drawn coordinates
coords <- c(5,5,119,256,119,256,120,340,478,
622,116,330,231,116,5,330,451,231,231,231)
coords <- matrix(coords, nc=2)
pause()
### Labels the same as names
V(g)$label <- V(g)$name
g$layout <- coords # $
pause()
### Take a look at it
plotG <- function(g) {
plot(g, asp=FALSE, vertex.label.color="blue", vertex.label.cex=1.5,
vertex.label.font=2, vertex.size=25, vertex.color="white",
vertex.frame.color="white", edge.color="black")
}
plotG(g)
pause()
### Add degree centrality to labels
V(g)$label <- paste(sep="\n", V(g)$name, degree(g))
pause()
### And plot again
plotG(g)
pause()
### Betweenness
V(g)$label <- paste(sep="\n", V(g)$name, round(betweenness(g), 2))
plotG(g)
pause()
### Closeness
V(g)$label <- paste(sep="\n", V(g)$name, round(closeness(g), 2))
plotG(g)
pause()
### Eigenvector centrality
V(g)$label <- paste(sep="\n", V(g)$name, round(evcent(g)$vector, 2))
plotG(g)
pause()
### PageRank
V(g)$label <- paste(sep="\n", V(g)$name, round(page.rank(g)$vector, 2))
plotG(g)
pause()
### Correlation between centrality measures
karate <- graph.famous("Zachary")
cent <- list(`Degree`=degree(g),
`Closeness`=closeness(g),
`Betweenness`=betweenness(g),
`Eigenvector`=evcent(g)$vector,
`PageRank`=page.rank(g)$vector)
pause()
### Pairs plot
pairs(cent, lower.panel=function(x,y) {
usr <- par("usr")
text(mean(usr[1:2]), mean(usr[3:4]), round(cor(x,y), 3), cex=2, col="blue")
} )
pause()
## ### A real network, US supreme court citations
## ## You will need internet connection for this to work
## vertices <- read.csv("http://jhfowler.ucsd.edu/data/judicial.csv")
## edges <- read.table("http://jhfowler.ucsd.edu/data/allcites.txt")
## jg <- graph.data.frame(edges, vertices=vertices, dir=TRUE)
## pause()
## ### Basic data
## summary(jg)
## pause()
## ### Is it a simple graph?
## is.simple(jg)
## pause()
## ### Is it connected?
## is.connected(jg)
## pause()
## ### How many components?
## no.clusters(jg)
## pause()
## ### How big are these?
## table(clusters(jg)$csize)
## pause()
## ### In-degree distribution
## plot(degree.distribution(jg, mode="in"), log="xy")
## pause()
## ### Out-degree distribution
## plot(degree.distribution(jg, mode="out"), log="xy")
## pause()
## ### Largest in- and out-degree, total degree
## c(max(degree(jg, mode="in")),
## max(degree(jg, mode="out")),
## max(degree(jg, mode="all")))
## pause()
## ### Density
## graph.density(jg)
## pause()
## ### Transitivity
## transitivity(jg)
## pause()
## ### Transitivity of a random graph of the same size
## g <- erdos.renyi.game(vcount(jg), ecount(jg), type="gnm")
## transitivity(g)
## pause()
## ### Transitivity of a random graph with the same degree distribution
## g <- degree.sequence.game(degree(jg, mode="out"), degree(jg, mode="in"),
## method="simple")
## transitivity(g)
## pause()
## ### Authority and Hub scores
## AS <- authority.score(jg)$vector
## HS <- hub.score(jg)$vector
## pause()
## ### Time evolution of authority scores
## AS <- authority.score(jg)$vector
## center <- which.max(AS)
## startyear <- V(jg)[center]$year
## pause()
## ### Function to go back in time
## auth.year <- function(y) {
## print(y)
## keep <- which(V(jg)$year <= y)
## g2 <- subgraph(jg, keep)
## as <- abs(authority.score(g2, scale=FALSE)$vector)
## w <- match(V(jg)[center]$usid, V(g2)$usid)
## as[w]
## }
## pause()
## ### Go back in time for the top authority, do a plot
## AS2 <- sapply(startyear:2005, auth.year)
## plot(startyear:2005, AS2, type="b", xlab="year", ylab="authority score")
## pause()
## ### Check another case
## center <- "22US1"
## startyear <- V(jg)[center]$year
## pause()
## ### Calculate past authority scores & plot them
## AS3 <- sapply(startyear:2005, auth.year)
## plot(startyear:2005, AS3, type="b", xlab="year", ylab="authority score")
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