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randomcircles.R
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rm(list = ls())
library(igraph)
require(ggplot2)
setwd("~/code/R/googleplus")
source("util.R")
# Select network:
# google
# facebook
# twitter
network <- "google"
cat("Network:",network)
cat("Loading graph...\n")
load(get.path(network,"graph.RDa"))
load(get.path(network,"circles.RDa"))
all.raw <- lapply(circles,function(x){mean(degree(graph,x,mode="all"))})
all <- sapply(seq(1,length(all.raw)),function(i){all.raw[[i]]})
all <- sort(all, decreasing = TRUE)
in.raw <- lapply(circles,function(x){mean(degree(graph,x,mode="in"))})
ind <- sapply(seq(1,length(in.raw)),function(i){in.raw[[i]]})
ind <- sort(ind, decreasing = TRUE)
out.raw <- lapply(circles,function(x){mean(degree(graph,x,mode="out"))})
out <- sapply(seq(1,length(out.raw)),function(i){out.raw[[i]]})
out <- sort(out, decreasing = TRUE)
par(mfrow = c(1,3))
plot(all, ylab= "Degree (log)",log="y")
plot(ind, ylab="In-Degree (log)", log="y")
plot(out, ylab="Out-Degree (log)", log="y")
load(get.path(network,"groupmetrics.RDa"))
plot(all,data$avgdegree)
plot(all,data$cutratio)
plot(all,data$conductance)
plot(all,data$modularity)
user.circle <- read.table(get.path(network,"user_circle.csv"),
header=F,sep=",",
col.names=c("user","circle"),
colClasses=c("character","character"))
######################################################################################
# Internal Connectivity / Average Degree
# f(C) = 2m_C/n_C
# n_C = # vertices in circle
# m_C = # edges in circle
######################################################################################
average.degree <- function(sub.graph){
g <- as.undirected(sub.graph,mode="collapse")
(2*ecount(g))/vcount(g)
}
######################################################################################
# External Connectivity / Cut Ratio
# f(C) = c_C/n_C(n-n_C)
# c_C = # edges on the boundary
# n_C = # vertices in circle
# n = # vertices
######################################################################################
cut.ratio <- function(graph, circle,cC){
cC/(length(circle)*(vcount(graph)-length(circle)))
}
######################################################################################
# Combined Internal and External Connectivity / Conductance
# f(C) = c_C/2m_C+c_C
# c_C = # edges on the boundary
# m_C = # edges in circle
######################################################################################
conductance <- function(sub.graph,cC){
cC/(2*ecount(sub.graph)+cC)
}
######################################################################################
# Network Model / Modularity
# f(C) = 1/4(m_C-E(m_C))
# m_C = # edges in circle
# E
######################################################################################
modu <- function(sub.graph,random.sub){
1/(2*ecount(graph)) * (ecount(sub.graph)-ecount(random.sub))
}
#sub.graph <- induced.subgraph(graph,V(graph)[circle])
#reciprocity <- sapply(V(sub.graph),relation.reciprocity, graph=sub.graph)
#data <- data.frame(circle=character(0),
# avgdegree=numeric(0),
# cutratio=numeric(0),
# conductance=numeric(0),
# modularity=numeric(0))
library(foreach)
library(doParallel)
cl <- makeCluster(6)
registerDoParallel(cl)
data <- foreach(circle = circles,.combine=rbind)%dopar%{
#data <- data.frame(circle=character(0),avgdegree=numeric(0),cutratio=numeric(0),conductance=numeric(0))
#for(circle in circles){
require(igraph)
cC <- length(E(graph)[xor(from(circle),to(circle))])
sub.graph <- induced.subgraph(graph,V(graph)[circle])
g1 <- degree.sequence.game(degree(graph,mode='out'),degree(graph,mode='in'),method="vl")
V(g1)$name <- V(graph)$name
g1.sub <- induced.subgraph(g1,V(g1)[circle])
ad <- average.degree(sub.graph)
cr <- cut.ratio(graph,circle,cC)
cd <- conductance(sub.graph,cC)
m <- modu(sub.graph,g1.sub)
data.frame(avgdegree=ad,
cutratio=cr,
conductance=cd,
modularity=m)
#print(a)
#data <- rbind(data,a)
}
stopCluster(cl)
#save(data,file=get.path(network,"groupmetrics.RDa"))
load(get.path(network,"groupmetrics.RDa"))
load("~/code/R/googleplus/twitter/egonetworks.RDa")
load(get.path(network,"circles.RDa"))
ego.random.circles <- function(ego.networks){
random.circles <- list()
for(i in 1:length(circles)){
ego.network <- sample(ego.networks,1)[[1]]
length <- length(circles[[i]])
if(length > length(ego.network)){
length <- length(ego.network)
}
random.circles[[i]] <- sample(ego.network,length)
}
random.cricles
}
walk.random.circles <- function(graph){
random.circles <- list()
lengths <- as.vector(sapply(circles,function(c){length(c)}))
count <- length(circles)
for(i in 1:count){
current <- sample(V(graph),1)
start <- current
circle <- c()
a <- sample(lengths,1)
for(x in 1:a){
print(current)
circle <- c(circle,current)
neighbors <- neighbors(graph,current,mode="all")
b <- length(neighbors)
neighbors <- setdiff(neighbors,circle)
cat("Neighbors: ", length(neighbors), " Circle: ", length(circle), "Diff:", b, "\n" )
while(is.null(neighbors) || length(neighbors) == 0){
cat("bäm")
current <- start
neighbors <- neighbors(graph,current,mode="all")
neighbors <- setdiff(neighbors,circle)
cat("Neighbors: ", length(neighbors), " Circle: ", length(circle), "Diff:", b, "\n" )
}
current <- sample(neighbors,1)
}
random.circles[[i]] <- circle
}
random.circles
}
random.circles <- walk.random.circles(graph)
#random.circles <- ego.random.circles(ego.networks)
library(foreach)
library(doParallel)
cl <- makeCluster(6)
registerDoParallel(cl)
walk.data.random <- foreach(circle = random.circles,.combine=rbind)%dopar%{
#data <- data.frame(circle=character(0),avgdegree=numeric(0),cutratio=numeric(0),conductance=numeric(0))
#for(circle in circles){
require(igraph)
cC <- length(E(graph)[xor(from(circle),to(circle))])
sub.graph <- induced.subgraph(graph,V(graph)[circle])
g1 <- degree.sequence.game(degree(graph,mode='out'),degree(graph,mode='in'))
V(g1)$name <- V(graph)$name
g1.sub <- induced.subgraph(g1,V(g1)[circle])
ad <- average.degree(sub.graph)
cr <- cut.ratio(graph,circle,cC)
cd <- conductance(sub.graph,cC)
m <- modu(sub.graph,g1.sub)
data.frame(avgdegree=ad,
cutratio=cr,
conductance=cd,
modularity=m)
#print(a)
#data <- rbind(data,a)
}
stopCluster(cl)
save(data.random,file=get.path(network,"walknetworkgroupmetricsrandom.RDa"))
save(data.random,file=get.path(network,"egonetworkgroupmetricsrandom.RDa"))
load(get.path(network,"groupmetricsrandom.RDa"))
load(get.path("google","groupmetrics.RDa"))
walk.data.random <- data.random
rancircle <- data.frame(type=as.factor(c(
rep("Circles",length(data[,1])),
rep("Random Walk",length(walk.data.random[,1]))
)),
value=c(data[order(data$avgdegree,decreasing=T),]$avgdegree,
walk.data.random[order(walk.data.random$avgdegree,decreasing=T),]$avgdegree))
require(plyr)
mydf_m <- ddply(rancircle,.(type),transform, ecd = ecdf(value)(value))
av <- ggplot(mydf_m,aes(x = value, y = ecd)) +
geom_line(aes(group = type,color = type,linetype = type), size=1.5) +
scale_x_continuous(limits = c(0, 331))+
xlab("Average Degree") +
ylab("CDF") +
theme_bw() +
theme(legend.title=element_blank(),legend.position=c(0.8,0.4),legend.key.width=unit(1,"in"))
rancircle <- data.frame(type=as.factor(c(
rep("Circles",length(data[,1])),
rep("Random Walk",length(walk.data.random[,1]))
)),
value=c(data[order(data$cutratio,decreasing=T),]$cutratio,
walk.data.random[order(walk.data.random$cutratio,decreasing=T),]$cutratio))
require(plyr)
mydf_m <- ddply(rancircle,.(type),transform, ecd = ecdf(value)(value))
rc <- ggplot(mydf_m,aes(x = value, y = ecd)) +
geom_line(aes(group = type,color = type, linetype = type), size=1.5) +
xlab("Ratio Cut") +
ylab("CDF") +
theme_bw() +
theme(legend.title=element_blank(),legend.position = "none")
rancircle <- data.frame(type=as.factor(c(
rep("Cricles",length(data[,1])),
rep("Random Walk",length(walk.data.random[,1]))
)),
value=c(data[order(data$conductance,decreasing=T),]$conductance,
walk.data.random[order(walk.data.random$conductance,decreasing=T),]$conductance))
require(plyr)
mydf_m <- ddply(rancircle,.(type),transform, ecd = ecdf(value)(value))
co <- ggplot(mydf_m,aes(x = value, y = ecd)) +
geom_line(aes(group = type,color = type,linetype = type), size=1.5) +
xlab("Conductance") +
ylab("CDF") +
theme_bw() +
theme(legend.title=element_blank(),legend.position = "none")
mod <- data.frame(type=as.factor(c(
rep("Cricles",length(data[,1])),
rep("Random Walk",length(walk.data.random[,1]))
)),
value=c(data[order(data$modularity,decreasing=T),]$modularity,
walk.data.random[order(walk.data.random$modularity,decreasing=T),]$modularity))
mydf_m <- ddply(mod,.(type),transform, ecd = ecdf(value)(value))
mo <- ggplot(mydf_m,aes(x = value, y = ecd)) +
geom_line(aes(group = type,color = type,linetype = type), size=1.5) +
xlab("Modularity") +
ylab("CDF") +
theme_bw() +
theme(legend.title=element_blank(),legend.position = "none")
require(gridExtra)
grid.arrange(av, rc, co, mo, ncol=2)