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[]

Now, let us consider an example of Instagram following among some individuals

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

NETWORK MEASURES

degrees

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