Hi,
I just found an intriguing community detection algorithm that tries to
reflect overlaps
A quick implementation in Python (and I assume it's similar in R):
# g is an undirected graph. If not, keep only the mutual edge pairs
before doing this
from igraph import *
# search for all cliques
cliques = [set(clique) for clique in g.cliques(min=3)]
# sort the cliques by length
cliques_by_length = {}
for clique in cliques:
k = len(clique)
if not cliques_by_length.has_key(k): cliques_by_length[k] = []
cliques_by_length[k].append(clique)
for k in cliques_by_length.keys():
# create clique adjacency graph for all k
adjacencies = []
cs = cliques_by_length[k]
for idx1, c1 in enumerate(cs):
for idx2, c2 in enumerate(cs):
if len(c1.union(c2)) == k+1:
adjacencies.append((idx1, idx2))
clique_graph = Graph(len(cs), adjacencies)
# determine the connected components
components = clique_graph.components()
print "k=%d:" % k
for component in components:
community = set()
for clique_idx in component: community.update(cs[clique_idx])
print " %s" % str(community)
This should work for small and medium sized sparse graphs. Note that
the clique detection routine in igraph uses substantially less
memory in the dev tree than in the last stable version, so you might
consider installing the development version.
--
T.
_______________________________________________
igraph-help mailing list
address@hidden
http://lists.nongnu.org/mailman/listinfo/igraph-help