====== Code Rnet 4 ======

===== Weak and strong components =====

<code R>
> wdir <- "C:/Users/batagelj/Documents/papers/2017/Moscow/Rnet/test"
> setwd(wdir)
> library(igraph)
> source("C:\\Users\\batagelj\\Documents\\papers\\2017\\Moscow\\Rnet\\test\\igraph+.R")
> R <- read.graph("./nets/class.net",format="pajek")
> vertex_attr(R)$shape <- NULL
> plot(R)
> w <- components(R,mode="weak")
> w
$membership
m02 m03 w07 w09 w10 w12 w22 w24 w28 w42 m51 w63 m85 m89 m96 
  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1 
$csize
[1] 15
$no
[1] 1
> s <- components(R,mode="strong")
> s
$membership
m02 m03 w07 w09 w10 w12 w22 w24 w28 w42 m51 w63 m85 m89 m96 
  3   4   4   4   4   4   4   4   4   4   2   4   4   1   4 
$csize
[1]  1  1  1 12
$no
[1] 4
> s
> V(R)$strong <- s$membership
> col <- c("red","green","orange","blue","green","magenta","grey","black")
> plot(R,vertex.color=col[s$membership])
> main <- extract_clusters(R,"strong",c(4))
> plot(main)
</code>

===== Measures of importance =====

[[https://cs.hse.ru/data/2015/05/14/1098547089/4._Centrality_Metrics.pdf|Metrics PDF]]
<code R>
> b <- betweenness(R,normalized=TRUE)
> plot(R,vertex.size=b*100)
> c <- closeness(R,normalized=TRUE)
> plot(R,vertex.size=c*100)
> e <- eigen_centrality(R)
> plot(R,vertex.size=e$vector*30)
> hub=hub.score(R)$vector
> plot(R,vertex.size=hub*20)
> aut=authority.score(R)$vector
> plot(R,vertex.size=aut*20)
> b <- bonpow(R,rescale=TRUE)
> plot(R,vertex.size=b*200)
> # clustering coefficient
> t <- transitivity(R,type="local")
> plot(R,vertex.size=t*25)

> a <- alpha_centrality(R,alpha=0.7)
> p <- power_centrality(R,exponent=0.7)
</code>

\\

[[ru:hse:rnet|Rnet]]