#! /usr/bin/Rscript
###
### Exploritory analysis of core vertices between the high school
### friendship and facebook networks. For one of the shared vertices
### in the facebook network, v, we randomly select 3 seeds from a 2-hop
### neighborhood around v so as to form the
### seed set S and then obtain the corresponding seed set S' in the
### survey network. We then match N_2(S) and N_2(S').
### We then add some excess vertices (10 from each Facebook unshared and
### Survey unshared, then all vertices) and using the same seed sets
### again match the 2-hop neighborhoods around the seed sets and
### evaluate the performance for these scenarios.
###
### Heather Gaddy Patsolic
### 2017 <[email protected]>
### S.D.G
#
require(raster)
require(igraph)
require(clue)
require(foreach)
require(ggplot2)
require(VN)
data('HSgraphs')
## User: If false, loads rdata file. If true, runs full script.
run <- FALSE
if(run){
#require(doMC); nCores <- 4
require(doMPI)
# Register Cores for parallelism
if("package:doMPI" %in% search()){
cl <- startMPIcluster()
registerDoMPI(cl)} else {
registerDoMC(nCores)
}
PERMUTE <- TRUE
set.seed(1234)
adj2 <- get.adjacency(HSfrgraphcore)
if(PERMUTE){
perm2 <- sample(vcount(HSfrgraphcore),vcount(HSfrgraphcore),replace=FALSE)
adj2 <- adj2[perm2,perm2]
#map <- cbind(1:vcount(HSfrgraphcore),perm2)
map2 <- cbind(perm2,1:vcount(HSfrgraphcore))
sp <- sort(perm2,index.return=TRUE)
map <- map2[sp$ix,]
colnames(map) <- c("core1id","core2id")
g.frscrambled <- graph_from_adjacency_matrix(adj2,mode="max")
}else{
map <- cbind(1:vcount(HSfrgraphcore),1:vcount(HSfrgraphcore))
colnames(map) <- c("core1id","core2id")
g.frscrambled <- HSfrgraphcore
}
hhop <- 2
ell <- 2
g <- 0.1
R <- 50
iter <- 20
voi <- 27
fbvoi <- map[voi,1]
frvoi <- map[voi,2]
Nhtmp <- unlist(ego(HSfbgraphcore,hhop,nodes=fbvoi,mindist=1)) # mindist=0: close, 1: open
cfblabelseeds <- map[Nhtmp,1]
cfrlabelseeds <- map[Nhtmp,2]
nbdx <- length(Nhtmp)
NhtmpR <- map[Nhtmp,2]
#NhtmpR <- unlist(ego(HSfrgraphcore,hhop,nodes=voi,mindist=1)) # mindist=0: close, 1: open
fbseedid <- map[Nhtmp,1]
frseedid <- map[NhtmpR,2]
len2 <- 5 #100 #50
comb <- list(combn(nbdx,2),combn(nbdx,3))
ss <- 1234
set.seed(ss)
seedsidx <- lapply(comb,function(x){sort(sample(ncol(x),100,replace=FALSE))})
nums <- 3
seedsidx <- seedsidx[[(nums-1)]]
lens <- length(seedsidx)
jvec <- c(5,10,15)
jvl <- length(jvec)
maxj <- 20
junkfb <- setdiff(V(HSfbgraphfull),coremap[,1])
junkfr <- setdiff(V(HSfriendsgraphfull),coremap[,2])
cjfb <- c(coremap[,1],junkfb)
cjfr <- c(coremap[,2],junkfr)
Afb <- as.matrix(get.adjacency(HSfbgraphfull))
Afr1 <- as.matrix(get.adjacency(HSfriendsgraphfull))
Afb <- Afb[cjfb,cjfb]
Afr2 <- Afr1[cjfr,cjfr]
Afr <- Afr2[c(perm2,setdiff(1:nrow(Afr2),perm2)),
c(perm2,setdiff(1:nrow(Afr2),perm2))]
cjfbS <- graph_from_adjacency_matrix(Afb,mode="undirected")
cjfrS <- graph_from_adjacency_matrix(Afr,mode="undirected")
#SET27j <- foreach(j = 1:maxj)%:%
SET27j <- foreach(ji = 1:jvl)%:%
foreach(le = 1:len2,.combine="rbind")%dopar%{
j <- jvec[ji]
set.seed(537*j+18400*le)
jfb <- sort(sample((length(coremap[,1])+1):length(cjfb),j))
jfr <- sort(sample((length(coremap[,2])+1):length(cjfr),j))
afb <- Afb[c(1:length(V(HSfbgraphcore)),jfb),
c(1:length(V(HSfbgraphcore)),jfb)]
afr <- Afr[c(1:length(V(HSfrgraphcore)),jfr),
c(1:length(V(HSfrgraphcore)),jfr)]
HScjfb <- graph_from_adjacency_matrix(afb,mode="undirected")
HScjfr <- graph_from_adjacency_matrix(afr,mode="undirected")
normrank <- rep(NA,length(seedsidx))
for(si in 1:length(seedsidx)){
sid <- seedsidx[si]
Sfb <- cfblabelseeds[comb[[2]][,sid]]
Sfr <- cfrlabelseeds[comb[[2]][,sid]]
frtmp <- sum(Sfr>frvoi)+frvoi
#Sfb <- which(cjfb %in% cfblabelseeds[comb[[2]][,sid]])
#nSfb <- setdiff(c(1:(82+j)),c(Sfb,fbvoi))
#afbS <- afb[c(Sfb,fbvoi,nSfb),c(Sfb,fbvoi,nSfb)]
#Sfr <- which(cjfr %in% cfrlabelseeds[comb[[2]][,sid]])
#frvoi <- which(cjfr==corefr[voi])
#nSfr <- setdiff(c(1:(82+j)),c(Sfr,frvoi))
#afrS <- afr[c(Sfr,frvoi,nSfr),c(Sfr,frvoi,nSfr)]
#cjfbS <- graph_from_adjacency_matrix(afbS,mode="undirected")
#cjfrS <- graph_from_adjacency_matrix(afrS,mode="undirected")
L <- localnbd(fbvoi,cbind(Sfb,Sfr),HScjfb,HScjfr,hhop,ell,R,g)
if(L$case=="possible"){
if(frtmp %in% L$labelsGxp){
#if(length(L$Cxp)==1){
# rankvoi <- 0
#}else{
Ps <- L$P
Ps <- Ps[1:length(L$labelsGx),1:length(L$labelsGxp)]
rankPs <- pass2ranksuplus(Ps)
rankvoi <- rankPs[(nums+1),
which(L$labelsGxp==(frtmp))]
## note, this assumes all seeds used which
## will be the case in our rigged example ---
## labeling identification is different if not
### all seeds used in vnsgm algorithm
rankvoi <- (rankvoi - 1)/(length(L$Cxp)-1)
}else{
rankvoi <- NA
}
}else{
rankvoi <- NA
}
normrank[si] <- rankvoi
}
nafreenr <- normrank[which(!is.na(normrank))]
mnr <- max(nafreenr)
punifmax <- function(x){
pumax <- punif(x,min=0,max=mnr)
pumax
}
pvalks <- ks.test(normrank,punifmax,alternative="greater")
numna <- sum(is.na(normrank))
list(pvalks=pvalks$p.value,normrank=normrank,numna=numna)
}
save.image("psubv27unshared_h2l2.RData")
}else{
load("psubv27unshared_h2l2.RData")
}
########################### 3 plots 0j, 10j, allj
load("../pvoi27_seedinc/psxinc_ss1234h12ell123.RData")
load("pv27fullgraphs_h2l2.RData")
require(ggplot2)
require(reshape2)
jadd <- 10
core <- Reduce(c,SET27[[2]][,1])
j5 <- Reduce(c,SET27j[[which(jvec==5)]][,2]$result.1)
j10 <- Reduce(c,SET27j[[which(jvec==jadd)]][,2]$result.1)
j15 <- Reduce(c,SET27j[[which(jvec==15)]][,2]$result.1)
jall <- Reduce(c,SET27ja[[3]]$normrank)
full <- cbind(core,j10,jall)
dfl <- data.frame(full)
colnames(dfl) <- c("0","10","all")
dfp1 <- melt(dfl)
## No id variables; using all as measure variables
colnames(dfp1) <- c("m","normrank")
full2 <- cbind(core,j5,j10,j15,jall)
dfl2 <- data.frame(full2)
colnames(dfl2) <- c("0","5","10","15","all")
dfp2 <- melt(dfl2)
## No id variables; using all as measure variables
colnames(dfp2) <- c("m","normrank")
##### Lattice plot
pjlat <-
ggplot(data=dfp1, aes(x=normrank, y = ..count.., colour=m,fill=m)) +
geom_histogram(binwidth=.05) + facet_wrap(~ m,nrow=1) +
theme(text=element_text(size=20)) +
#theme(axis.text.x = element_text(angle=90, hjust=-10)) +
xlab(expression(paste(tau,"(x')"))) +
ggtitle("HS with VOI=27 -- Inc m")
pjlat2 <-
ggplot(data=dfp2, aes(x=normrank, y = ..count.., colour=m,fill=m)) +
geom_histogram(binwidth=.05) + facet_wrap(~ m,nrow=1) +
theme(text=element_text(size=20)) +
xlab(expression(paste(tau,"(x')"))) +
ggtitle("HS with VOI=27 -- Inc m")
#pdf('./pHSundirJinc_voi27lattice_example10_h2l2.pdf',height=4.3,width=11)
print(pjlat)
## Warning: Removed 31 rows containing non-finite values (stat_bin).
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)
#pdf('./HSundirJinc_voi27lattice_example10_h2l2.pdf',height=4,width=6)
#print(pjlat)
#dev.off()
#pdf('./pHSundirJinc_voi27lattice_example51015_h2l2.pdf',height=5,width=17)
#print(pjlat2)
#dev.off()
#if(run){
# if(exists("cl")){
# closeCluster(cl)
# mpi.quit()
# }
#}
# Time:
## Working status:
### Comments:
####Soli Deo Gloria