Social Network Analysis

#install.packages("igraph")
library(igraph)
g <- graph(c(1,2,2,3,3,4,4,1),
           directed = T,
           n=7)#n is the number of nodes
plot(g,vertex.size=40,
     vertex.color='green',
     edge.color='red')

g[]

g2 <- graph(c("Amy", "Ram", "Ram", "Li", "Li", "Amy",
              "Amy", "Li", "Kate", "Li"),
            directed=T)
plot(g2,vertex.size=40,
     vertex.color='green',
     edge.color='red')

g2

edge_density(g2, loops = F)

ecount(g2)/(vcount(g2)*(vcount(g2)-1))


#0.4166667

reciprocity(g2)#calculates % of ties in directed graphs,proportion of mutual connections


#0.4


closeness(g2,mode='all',weights=NA)#closeness centrality measures how many steps is required to access every other vertex
#from a given vertex

betweenness(g2,directed=T,weights=NA)# vertex or edge betweeness is the number of shortest paths going through a vertex or an edge

edge_betweenness(g2,directed=T,weights=NA)

#Load Data
data<-read.csv(file.choose(),header=T)

#Create Dataframe
y<-data.frame(data$first,data$second)

#Create Network
net<- graph.data.frame(y,directed=T)
V(net)#Vertices
E(net)#Edges
V(net)$label<-V(net)$name
V(net)$degree<- degree(net)

#Histogram of node degree
hist(V(net)$degree,
     col='green',
     ylab='Frequency',
     xlab='Degree of vertices',
     main='Histogram of Node degree')

#Network Diagram 
set.seed(222)
plot(net,vertex.color='green',
     vertext.size=2,
     vertex.label.cex=0.8,
     edge.arrow.size=0.1)

plot(net,
     vertex.color=rainbow(52),
     vertex.size=V(net)$degree*0.4,
     edge.arrow.size=0.1,
     layout=layout.fruchterman.reingold)

plot(net,
     vertex.color=rainbow(52),
     vertex.size=V(net)$degree*0.4,
     edge.arrow.size=0.1,
     layout=layout.graphopt)

plot(net,
     vertex.color=rainbow(52),
     vertex.size=V(net)$degree*0.4,
     edge.arrow.size=0.1,
     layout=layout.kamada.kawai)

#Hubs(number of outgoing) and Authorities(number of incoming to hubs)
hs<-hub_score(net)$vector
as<-authority.score(net)$vector

par(mfrow=c(1,2))
set.seed(123)
plot(net,
     vertex.size=hs*30,
     mains='Hubs',
     vertex.color=rainbow(52),
     edge.arrow.size=0.1,
     layout=layout.kamada.kawai)
plot(net,
     vertex.size=as*30,
     mains='Authorities',
     vertex.color=rainbow(52),
     edge.arrow.size=0.1,
     layout=layout.kamada.kawai)

#Community Detection
net<-graph.data.frame(y,directed=F)
cnet<-cluster_edge_betweenness(net)
plot(cnet,
     net,
     vertex.size=10,
     vertex.label.cex=0.8)

0.41666670.4166667