Social Network Analyisis

Load Data

#load Data
library(readr)
library(DT)
data <- read_csv("C:/Users/lenovo/Downloads/socialnetworkdata.csv")

datatable(data)

Extract two columns of data and create a data.frame

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

datatable(y)

Create network graph. Note that a vertex is a node and an edge is a link between vertices

require(igraph)
net <- graph.data.frame(y,directed = T)

#display vertex
V(net)

## + 52/52 vertices, named, from 8fd9f10:
##  [1] AA AB AF DD CD BA CB CC BC ED AE CA EB BF BB AC DC BD DB CF DF BE EA CE EE
## [26] EF FF FD GB GC GD AD KA KF LC DA EC FA FB DE FC FE GA GE KB KC KD KE LB LA
## [51] LD LE

#label the vertex
V(net)$label < V(net)$name

## logical(0)

#add the weight, Degree represents the number of conections to a node
V(net)$degree <- degree(net)

V(net)$degree <- degree(net)
V(net)$degree

##  [1] 18  9 23 36 40 26 24 50 21 27 15 62  7 12 23 27  2  4  8 12 23 20  8 10  6
## [26]  8  1  8  1  1  1  9  3  3  1  7  3  1  1  2  1  2  5  1  1  1  1  1  1  1
## [51]  1  1

#Add the weight, Degree represents the number of conections to a node
hist(V(net)$degree,
     col = 'skyblue',
     main = 'Histogram of Node Degree',
     ylab = 'Number of Nodes',
     xlab = 'Degree of Vertices')

Network Diagram Only shows connections

set.seed(222)
plot(net,
     vertex.color = 'green',
     vertex.size =15,
     edge.arrow.size = 0.1,
     vertex.label.cex =0.8)

Weighted details are in this chart

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