[igraph] Translation of R commands into Python

[Lorenzo Isella]

Dear All,
Some time ago I posted a small R script with a basic analysis of a real 
graph. I am trying to do the same with Python, but I am experiencing 
some problems, mainly due to the fact that I am not at ease with the 
automatically generated documentation of the Python module and I have 
not been able to find much online (have I looked in the wrong places?).
Below you can find a list of igraph commands under R (mainly taken from 
that previous email of mine). Apart from reading the graph from a 
two-column matrix, all the other commands simply assume that there is a 
graph g.
What I would really like to see is how these commands for the igraph R 
bindings translate into commands for the Python bindings.
Many thanks

Lorenzo



# Now I have the graph as a two-column matrix

g <- graph(t(graph_input_unweighted), directed=FALSE)

g <- simplify(g)

#degree of each vertex

dd <- degree(g)


pfit <- power.law.fit(dd)

#Now calculate graph diameter


d <- get.diameter(g)


#Now calculate the vertex betweenness
#and find the node with the highest betweenness

ver_bet <- betweenness(g)


#Now calculate the clustering coefficients (also called transitivity of 
a graph)

clust <- transitivity(g, type="local")

# calculate average nearest-neighbour degree

knn <- graph.knn(g)

#knn$knnk lists the average nearest neighbour degree for vertices having 
at least one neighbour

#Now get the shortest paths in the network

mean_shortest_path <- average.path.length(g)


#Now consider the neighbourhood of the graph (the final purpose is to 
calculate
# the average number of nodes within a distance x from a general node in 
the graph )

#I know the graph diameter, i.e. the longest shortest path. Since d 
stores actually
# the nodes in the graph diameter, the length of the diameter (number of 
edges)
# is actually length(d)-1

M_l <- seq(length(d)-1)

for (i in seq(length(d)-1)){

M_l[i] <- mean(neighborhood.size(g, i, nodes=V(g)))

}