library(igraph)
A <- read.csv(file="WorkingData.csv",header=F,sep=",")
M<-as.matrix(A)
nr=nrow(M)
nc=ncol(M)
g1 <- graph_from_incidence_matrix(M,directed=FALSE,mode="out",weighted=TRUE)
#Constructing Bipartite Graph
bipartite.mapping(g1)
V(g1)$type<-bipartite_mapping(g1)$type
plot(g1,layout=layout.bipartite)
types <- V(g1)$type
#Calculating Degree, betweenness, closeness, eigen centraility measures
deg<- degree(g1)
bet<-betweenness(g1)
clos<-closeness(g1)
eig<-eigen_centrality(g1)$vector
cent_df<-data.frame(types, deg, bet, clos, eig)
cent_df[order(cent_df$type,decreasing=TRUE),]
library(ade4)
bipartite_matrix<-as_incidence_matrix(g1)
#Calculating Jaccard cofficient and measures such as Degree, betweenness, closeness, eigen 
#centraility measures
person_jacc<-dist.binary(bipartite_matrix,method=1,upper=TRUE,diag=FALSE)
org_jacc<-dist.binary(t(bipartite_matrix),method=1,upper=TRUE,diag=FALSE)
person_jacc<-as.matrix(person_jacc)
jacc_person<-ifelse(person_jacc>0.95,1,0)
diag(jacc_person)<-0
jacc_person
jacc_person<-graph_from_adjacency_matrix(jacc_person,mode="undirected")
plot(jacc_person)
person_deg<-degree(jacc_person)
person_bet<-betweenness(jacc_person)
person_clos<-closeness(jacc_person)
person_eig<-eigen_centrality(jacc_person)$vector
person_cent_df<-data.frame(person_deg, person_bet, person_clos, person_eig)
library(topsis)
#Evaluation matrix for clustering and grading
A <- read.csv(file="topsi.csv",header=F,sep=",")
m<-as.matrix(A)
w<-c(1,1,1,1)
i<-c("+","+","+","+")
top<-topsis(m,w,i)
top_df<-data.frame(top$alt.row,top$score,top$rank)
cwt <- cluster_walktrap(g1,weights=E(g1)$weight,steps=6)
library(cluster)
IMat<-m
cfuz<-fanny(IMat,2,memb.exp=1.1)
names(cfuz)
plot(cfuz)
fmem<-cfuz$membership
fcoeff<-cfuz$coeff
fmembexp<-cfuz$memb.exp
fclust<-cfuz$clustering
fkcrisp<-cfuz$k.crisp
fobj<-cfuz$objective
fconv<-cfuz$convergence
fdiss<-cfuz$diss
fcall<-cfuz$call
fsilinfo<-cfuz$silinfo
fdata<-cfuz$data
AC <- read.csv(file="Cluster.csv",header=F,sep=",")
MAC<-as.matrix(AC)
g<-graph(MAC)
plot(MAC)
cwt(g)
cwt <- cluster_walktrap(g,weights=E(g)$weight,steps=10)