library(igraph)

# read the graph from a file
# here we have the network stored in an edgelist
g <- read.graph("/Users/kpele/Documents/Teaching/i3-NetScience/sample_net",format="ncol")
# visualize the network
plot(g)
# compute the degree of every node in the network
degree(g)
# compute the normalized frequency of every degree in the network
degree.distribution(g)
# plot the degree distribution
barplot(degree.distribution(g),names=c(0,1,2,3,4),xlab="Node degree",ylab="Normalized frequency")
# examine whether the network is connected, i.e., there is only one connected component
is.connected(g)
# find the components of the network
clusters(g)
# find the average path length
average.path.length(g)
# find the shortest path length between every pair of nodes
shortest.paths(g)
# find the actual shortest paths from node "from" to all the other nodes in the network
get.shortest.paths(g,from=V(g)[1])
# find the local clustering coefficient of each node in the network
transitivity(g,type="local")