An Introduction to Social Network Analysis

SNA ex.r

# *------------------------------------------------------------------
# | PROGRAM NAME: SNA ex
# | DATE: 4/9/12
# | CREATED BY: MATT BOGARD 
# | PROJECT FILE: P:\R  Code References\SNA            
# *----------------------------------------------------------------
# | PURPOSE: DEMONSTRATION OF BASIC CONCEPTS OF NETWORK ANALYSIS  
# | Code to support: http://econometricsense.blogspot.com/2012/04/introduction-to-social-network-analysis.html 
# | REFERENCES: Conway, Drew. Social Network Analysis in R. 
# | New York City R User Group Meetup Presentation August 6, 2009
# | http://www.drewconway.com/zia/wp-content/uploads/2009/08/sna_in_R.pdf
# *------------------------------------------------------------------
 
#------------------------------------------------
# matrix algebra 
#------------------------------------------------
 
# specify the adjacency matrix
A <- matrix(c(0,1,1,1,1,0,1,0,1,1,0,0,1,0,0,0 ),4,4, byrow= TRUE)
EV <- eigen(A) # compute eigenvalues and eigenvectors
max(EV$values)  # find the maximum eigenvalue
 
# get the eigenvector associated with the largest eigenvalue
centrality <- data.frame(EV$vectors[,1]) 
names(centrality) <- "Centrality"
print(centrality)
 
#------------------------------------------------
# igraph tools
#------------------------------------------------
 
library(igraph)
 
G<-graph.adjacency(A, mode=c("undirected"))               # convert adjacency matrix to an igraph object
cent<-data.frame(bet=betweenness(G),eig=evcent(G)$vector) # calculate betweeness & eigenvector centrality 
res<-as.vector(lm(eig~bet,data=cent)$residuals)           # calculate residuals
cent<-transform(cent,res=res)                             # add to centrality data set
write.csv(cent,"r_keyactorcentrality.csv")                # save in project folder
 
plot(G, layout = layout.fruchterman.reingold)             # network visualization
 
# create vertex names and scale by centrality
plot(G, layout = layout.fruchterman.reingold, vertex.size = 20*evcent(G)$vector, vertex.label = as.factor(rownames(cent)), main = 'Network Visualization in R')
 
# key actor analysis - plot eigenvector centrality vs. betweeness
# and scale by residuals from regression: eig~bet
 
library(ggplot2)
 
p<-ggplot(cent,aes(x=bet,y=eig,label=rownames(cent),colour=res,
                   size=abs(res)))+xlab("Betweenness Centrality")+ylab("Eigenvector Centrality")
pdf('key_actor_analysis.pdf')
p+geom_text()+opts(title="Key Actor Analysis")   
 
dev.off()