Cluster your Facebook friends

friendscluster.R

require(igraph)
require(RCurl)
require(rjson)

# Insert your own token                                                                                                                               
access_token <- '****'

# Romain François's function to connect to Facebook Graph API                                                                                         
facebook <- function( path = "me", access_token = token, options) {
  if( !missing(options) ){
    options <- sprintf( "?%s", paste( names(options), "=", unlist(options), collapse = "&", sep = "" ) )
  } else {
    options <- ""
  }
  data <- getURL( sprintf( "https://graph.facebook.com/%s%s&access_token=%s", path, options, access_token ) )
  fromJSON( data )
}

# Petr Simecek's code to retrieve friends' relationships                                                                                              
# scrape the list of friends                                                                                                                          
friends <- facebook( path="me/friends" , access_token=access_token)
# extract Facebook IDs                                                                                                                                
friends.id <- sapply(friends$data, function(x) x$id)
# extract names                                                                                                                                       
friends.name <- sapply(friends$data, function(x)  iconv(x$name,"UTF-8","ASCII//TRANSLIT"))
# short names to initials                                                                                                                             
initials <- function(x) paste(substr(x,1,1), collapse="")
friends.initial <- sapply(strsplit(friends.name," "), initials)


# friendship relation matrix                                                                                                                          
N <- length(friends.id)
friendship.matrix <- matrix(0,N,N,dimnames=list(friends.id, friends.id))
for (i in 1:N) {
  tmp <- facebook( path=paste("me/mutualfriends", friends.id[i], sep="/") , access_token=access_token)
  mutualfriends <- sapply(tmp$data, function(x) x$id)
  friendship.matrix[i,friends.id %in% mutualfriends] <- 1
}

ga.data <- do.call(rbind, lapply(1:N, function(i) {
  do.call(rbind, lapply(1:N, function(j) {
    if(friendship.matrix[i,j]==1) return(data.frame(from=friends.name[i], to=friends.name[j]))
  }))
}))


# Gary's code for social network analysis and clustering                                                                                              
g <- graph.data.frame(ga.data, directed=FALSE)

gnc <- edge.betweenness.community(g, directed=FALSE)
m <- vector()
for (s in 0:nrow(gnc$merges) ) {
          memb <- community.to.membership(g,gnc$merge,steps=s)$membership
          m <- c(m,modularity (g, memb, weights=NULL))
        }
ideal <- steps <- which(m==max(m)) - 1
gn.groups <- community.to.membership(g,gnc$merge, steps=ideal <- steps)$membership
V(g)$color <- gn.groups
#V(g)$label <- V(g)$name # this is to show the friends' name on the plot                                                                              
V(g)$label <- NA
V(g)$size <- 3
png('friendscluster.png')
plot(g)
dev.off()

Wonderful project, I have forked and will be working to see if I could add something here.