g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = m_i, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
#m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = 2, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
#m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = 2, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
#m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = 2, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
#m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = 2, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
#m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = 2, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
repeat {
#m_i <- sample(1:2, 1)
g <- sample_pa(n = 10, power = 1, m = 2, directed = FALSE)
if (max(degree(g)) <= 4) {
break
}
}
# 可视化
plot(g, vertex.label=V(g)$name, main="Scale-free Network (m=2, max degree ≤ 4)")
# Obtain the adjacency matrix
adjacency_matrix <- get.adjacency(g, sparse = FALSE)
sum(adjacency_matrix)
rm(list = ls())
library(R.matlab)
mat_data <- readMat("coautorship_data_130.mat")
table(mat_data$z)
sum_edges<-sum(mat_data$A.bin)
indices1<-which(mat_data$z==1)
indices2<-which(mat_data$z==2)
inter_edges1<-sum(mat_data$A.bin[indices1,indices1])
inter_edges2<-sum(mat_data$A.bin[indices2,indices2])
indices1<-which(mat_data$z==1)
indices2<-which(mat_data$z==2)
indices3<-which(mat_data$z==3)
indices4<-which(mat_data$z==4)
indices5<-which(mat_data$z==5)
indices10<-which(mat_data$z==10)
intra_edges1<-sum(mat_data$A.bin[indices1,indices1])
intra_edges2<-sum(mat_data$A.bin[indices2,indices2])
intra_edges3<-sum(mat_data$A.bin[indices3,indices3])
intra_edges4<-sum(mat_data$A.bin[indices4,indices4])
inter_edges5<-sum(mat_data$A.bin[indices5,indices5])
inter_edges10<-sum(mat_data$A.bin[indices10,indices10])
intra_edges5<-sum(mat_data$A.bin[indices5,indices5])#145
intra_edges10<-sum(mat_data$A.bin[indices10,indices10])#1
mean_inter_density<-mean(intra_edges1/2/2,intra_edges2/2/2,intra_edges3/38/38,intra_edges4/40/40,intra_edges5/46/46,
intra_edges10/2/2)/2
mean_inter_density
mean_inter_density<-mean(intra_edges1/2/2,intra_edges2/2/2,intra_edges3/38/38,intra_edges4/40/40,intra_edges5/46/46,
intra_edges10/2/2)/2
intra_edges3/38/38
intra_edges4/40/40
intra_edges5/46/46
intra_edges10/2/2
mean_intra_density<-mean(intra_edges1/2/2,intra_edges2/2/2,intra_edges3/38/38,intra_edges4/40/40,intra_edges5/46/46,
intra_edges10/2/2)
mean_intra_density<-sum(intra_edges1/2/2,intra_edges2/2/2,intra_edges3/38/38,intra_edges4/40/40,intra_edges5/46/46,
intra_edges10/2/2)
mean_intra_density<-sum(intra_edges1/2/2,intra_edges2/2/2,intra_edges3/38/38,intra_edges4/40/40,intra_edges5/46/46,
intra_edges10/2/2)/12
indices<-list(indices1,indices2,indices3,indices4,indices5,indices10)
indices[1]
inter_density<-rep(0,30)
group_num<-as.data.frame(table(vec))
group_num<-as.data.frame(table(mat_data$z))
group_num
group_num<-as.data.frame(table(mat_data$z))$Freq
inter_density<-rep(0,30)
for (i in 1:5) {
for (j in i+1:6) {
sub_net<-mat_data$A.bin[indices[i],indices[j]]
edges<-sum(sub_net)/2
total<-group_num[i]*group_num[j]
inter_density[5*(i-1)+j]<-edges/total
}
}
View(indices)
indices[[1]]
indices[1]
inter_density<-rep(0,30)
for (i in 1:5) {
for (j in i+1:6) {
sub_net<-mat_data$A.bin[indices[[i]],indices[[j]]]
edges<-sum(sub_net)/2
total<-group_num[i]*group_num[j]
inter_density[5*(i-1)+j]<-edges/total
}
}
for (i in 1:5) {
for (j in (i+1):6) {
sub_net<-mat_data$A.bin[indices[[i]],indices[[j]]]
edges<-sum(sub_net)/2
total<-group_num[i]*group_num[j]
inter_density[5*(i-1)+j]<-edges/total
}
}
mean(inter_density)
mat_data <- readMat("contact311Cs4.mat")
table(mat_data$z)
sum_edges<-sum(mat_data$A.bin)
indices<-which(mat_data$z==1)
inter_edges2<-sum(mat_data$A.bin[indices,indices])
inter_edges1<-sum(mat_data$A.bin[indices,indices])
inter_edges1<-sum(mat_data$A.bin[indices,indices])
inter_edges2<-sum(mat_data$A.bin[indices,indices])
mean_intra_density<-sum(intra_edges1/2/1,intra_edges2/2/1,intra_edges3/38/37,intra_edges4/40/39,intra_edges5/46/45,
intra_edges10/2/1)/12
intra_prop<-inter_edges1/141/140/2+inter_edges2/170/169/2
indices1<-which(mat_data$z==0)
indices2<-which(mat_data$z==1)
inter_edges<-sum(mat_data$A.bin[indices1,indices2])/2
inter_density<-rep(0,30)
for (i in 1:5) {
for (j in (i+1):6) {
sub_net<-mat_data$A.bin[indices[[i]],indices[[j]]]
edges<-sum(sub_net)
total<-group_num[i]*group_num[j]
inter_density[5*(i-1)+j]<-edges/total
}
}
mean(inter_density)
inter_edges<-sum(mat_data$A.bin[indices1,indices2])
inter_dense<-inter_edges/141/170
rm(list = ls())
setwd("E:/PROJECT/BiLevel/real data")
load('brca.Rdata')
View(BRCA)
adj<-BRCA$Adj
group<-BRCA$gene_pathway[2]
View(group)
table(group)
indices<-list()
groups<-as.data.frame(table(group))
View(groups)
group_name<-groups$pathway
group_num<-groups$Freq
View(indices)
View(groups)
group_name
length(group_name)
G<-length(group_name)
View(group)
for (i in 1:G) {
indices[i]=which(group==group_name[i])
}
group_name<-as.vector(groups$pathway)
for (i in 1:G) {
indices[i]=which(group==group_name[i])
}
View(indices)
indices[1]
indices<-list()
for (i in 1:G) {
indices<- append(indices,which(group==group_name[i])_
}
for (i in 1:G) {
indices<- append(indices,which(group==group_name[i])
}
for (i in 1:G) {
indices<- append(indices,which(group==group_name[i]))
}
View(indices)
which(group==group_name[i])
indices<-list()
indices<-list()
for (i in 1:G) {
indices<- append(indices,c(which(group==group_name[i])))
}
indices<-list()
for (i in 1:G) {
indices[[i]]<- which(group==group_name[i])
}
View(indices)
intra_density<-rep(0,G)
intra_edges<-rep(0,G)
intra_density<-rep(0,G)
for (i in 1:G) {
intra_edges[i]<-sum(adj[indices[[i]],indices[[i]]])/2
total<-group_num[i]*(group_num[i]-1)/2
intra_density[i]<-intra_edges[i]/total
}
mean(intra_density)
inter_edges<-rep(0,G*(G-1)/2)
inter_density<-rep(0,G*(G-1)/2)
for (i in 1:(G-1)) {
for (j in (i+1):G) {
sub_net<-mat_data$A.bin[indices[[i]],indices[[j]]]
inter_edges[(G-1)*(i-1)+j]<-sum(sub_net)
total<-group_num[i]*group_num[j]
inter_density[(G-1)*(i-1)+j]<-edges/total
}
}
for (i in 1:(G-1)) {
for (j in (i+1):G) {
sub_net<-adj[indices[[i]],indices[[j]]]
inter_edges[(G-1)*(i-1)+j]<-sum(sub_net)
total<-group_num[i]*group_num[j]
inter_density[(G-1)*(i-1)+j]<-edges/total
}
}
for (i in 1:(G-1)) {
for (j in (i+1):G) {
sub_net<-adj[indices[[i]],indices[[j]]]
inter_edges[(G-1)*(i-1)+j]<-sum(sub_net)
total<-group_num[i]*group_num[j]
inter_density[(G-1)*(i-1)+j]<-inter_edges[(G-1)*(i-1)+j/total
}
}
for (i in 1:(G-1)) {
for (j in (i+1):G) {
sub_net<-adj[indices[[i]],indices[[j]]]
inter_edges[(G-1)*(i-1)+j]<-sum(sub_net)
total<-group_num[i]*group_num[j]
inter_density[(G-1)*(i-1)+j]<-inter_edges[(G-1)*(i-1)+j]/total
}
}
inter_edges<-rep(0,G*(G-1)/2)
inter_density<-rep(0,G*(G-1)/2)
for (i in 1:(G-1)) {
for (j in (i+1):G) {
sub_net<-adj[indices[[i]],indices[[j]]]
inter_edges[(G-1)*(i-1)+j]<-sum(sub_net)
total<-group_num[i]*group_num[j]
inter_density[(G-1)*(i-1)+j]<-inter_edges[(G-1)*(i-1)+j]/total
}
}
inter_edges<-rep(0,G*(G-1)/2)
inter_density<-rep(0,G*(G-1)/2)
for (i in 1:(G-1)) {
for (j in (i+1):G) {
sub_net<-adj[indices[[i]],indices[[j]]]
inter_edges[(G-1)*(i-1)+(j-i)]<-sum(sub_net)
total<-group_num[i]*group_num[j]
inter_density[(G-1)*(i-1)+(j-i)]<-inter_edges[(G-1)*(i-1)+j]/total
}
}
inter_edges<-rep(0,G*(G-1)/2)
inter_density<-rep(0,G*(G-1)/2)
for (i in 1:(G-1)) {
for (j in (i+1):G) {
sub_net<-adj[indices[[i]],indices[[j]]]
inter_edges[(G-1)*(i-1)+(j-i)]<-sum(sub_net)
total<-group_num[i]*group_num[j]
inter_density[(G-1)*(i-1)+(j-i)]<-inter_edges[(G-1)*(i-1)+(j-i)]/total
}
}
inter_edges<-c()
inter_density<-c()
for (i in 1:(G-1)) {
for (j in (i+1):G) {
sub_net<-adj[indices[[i]],indices[[j]]]
edges<-sum(sub_net)
inter_edges<-append(inter_edges,edges)
total<-group_num[i]*group_num[j]
inter_density<-append(inter_density,edges/total)
}
}
mean(inter_density)
mean(intra_density)
rm(list = ls())
library(R.matlab)
mat_data <- readMat("coautorship_data_130.mat")
table(mat_data$z)
sum_edges<-sum(mat_data$A.bin)#525
indices1<-which(mat_data$z==1)
indices2<-which(mat_data$z==2)
indices3<-which(mat_data$z==3)
indices4<-which(mat_data$z==4)
indices5<-which(mat_data$z==5)
indices10<-which(mat_data$z==10)
intra_edges1<-sum(mat_data$A.bin[indices1,indices1])#0
intra_edges2<-sum(mat_data$A.bin[indices2,indices2])#0
intra_edges3<-sum(mat_data$A.bin[indices3,indices3])#70
intra_edges4<-sum(mat_data$A.bin[indices4,indices4])#69
intra_edges5<-sum(mat_data$A.bin[indices5,indices5])#145
intra_edges10<-sum(mat_data$A.bin[indices10,indices10])#1
mean_intra_density<-sum(intra_edges1/2/1,intra_edges2/2/1,intra_edges3/38/37,intra_edges4/40/39,intra_edges5/46/45,
intra_edges10/2/1)/12
group_num<-as.data.frame(table(mat_data$z))$Freq
indices<-list(indices1,indices2,indices3,indices4,indices5,indices10)
inter_density<-c()
group_num<-as.data.frame(table(mat_data$z))$Freq
indices<-list(indices1,indices2,indices3,indices4,indices5,indices10)
inter_density<-c()
for (i in 1:5) {
for (j in (i+1):6) {
sub_net<-mat_data$A.bin[indices[[i]],indices[[j]]]
edges<-sum(sub_net)
total<-group_num[i]*group_num[j]
inter_density<-append(inter_density,edges/total)
}
}
mean(inter_density)
rm(list = ls())
setwd("E:/PROJECT/BiLevel/real data")
load('LUAD.Rdata')
adj<-LUAD$Adj
group<-LUAD$gene_pathway[2]
table(group)
groups<-as.data.frame(table(group))
group_name<-as.vector(groups$pathway)
group_num<-groups$Freq
G<-length(group_name)
indices<-list()
for (i in 1:G) {
indices[[i]]<- which(group==group_name[i])
}
intra_edges<-rep(0,G)
intra_density<-rep(0,G)
for (i in 1:G) {
intra_edges[i]<-sum(adj[indices[[i]],indices[[i]]])/2
total<-group_num[i]*(group_num[i]-1)/2
intra_density[i]<-intra_edges[i]/total
}
mean(intra_density)
inter_edges<-c()
inter_density<-c()
for (i in 1:(G-1)) {
for (j in (i+1):G) {
sub_net<-adj[indices[[i]],indices[[j]]]
edges<-sum(sub_net)
inter_edges<-append(inter_edges,edges)
total<-group_num[i]*group_num[j]
inter_density<-append(inter_density,edges/total)
}
}
mean(inter_density)
sum(inter_edges)
sum(inter_edges)/(sum(inter_edges)+sum(intra_edges))
sum(inter_edges)+sum(intra_edges)
rm(list = ls())
library(R.matlab)
mat_data <- readMat("contact311Cs4.mat")
View(mat_data)
mat_data <- readMat("contact311.mat"
)
View(mat_data)
View(BRCA)
BRCA_DATA<-BRCA[-3]
load("E:/PROJECT/BiLevel/real data/LUAD.Rdata")
LUAD_DATA<-LUAD[-3]
setwd("E:/PROJECT/BiLevel/DeepBLNet/data")
save(LUAD_DATA,file = 'LUAD.Rdata')
save(BRCA_DATA,file = 'BRCA.Rdata')
