Community Detection In Networkx

I'm studying about detection communities in networks. I'm use igraph and Python For the optimal number of communities in terms of the modularity measure: from igraph import * karat

Solution 1:

I'm also new to networkx and igraph, I used Gephi, an data visualization tool/software. And it has the same community detection algorithm as the one in networkx you are now using. Specifically, in http://perso.crans.org/aynaud/communities/

It uses the louvain method described in Fast unfolding of communities in large networks, Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Renaud Lefebvre, Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10008 (12pp)

You can not get desired number of communities, as I know, there're two ways worth to try:

• Use Gephi. You can use gephi and there's a parameter called resolution that would change the size of the community you get.
• Use NetworkX. This time, we may not use best_partition(G) any more. But use partition_at_level(dendrogram, level) , I guess this might help.

Check the source code here for more info.

Solution 2:

Perhaps I am misunderstanding you, but if you would like the number of communities output by the NetworkX implementation of the best_partition algorithm, just note that best_partition(G) gives a dictionary with nodes as keys and their partition number as value.

You can count the number of unique values in a dictionary like this (likely not optimal):

dict = {'a':1,'b':1,'c':2,'d':1,'e':3,'f':4,'g':5}
count=list(set([i for i indict.values()]))
print count
printlen(count)

With result

[1, 2, 3, 4, 5]
5