def test_lais2(self):
g = get_string_graph()
com = algorithms.lais2(g)
self.assertEqual(type(com.communities), list)
if len(com.communities) > 0:
self.assertEqual(type(com.communities[0]), list)
self.assertEqual(type(com.communities[0][0]), str)
elif (options.method == 'rb_pots'):
communities = algorithms.rb_pots(g, weights='weight', **clust_kwargs)
elif (options.method == 'significance_communities'):
communities = algorithms.significance_communities(g, **clust_kwargs)
elif (options.method == 'spinglass'):
communities = algorithms.spinglass(g, **clust_kwargs)
elif (options.method == 'surprise_communities'):
communities = algorithms.surprise_communities(g, **clust_kwargs)
elif (options.method == 'walktrap'):
communities = algorithms.walktrap(g, **clust_kwargs)
#elif(options.method == 'sbm_dl'):
# communities = algorithms.sbm_dl(g)
#elif(options.method == 'sbm_dl_nested'):
# communities = algorithms.sbm_dl_nested(g)
elif (options.method == 'lais2'):
communities = algorithms.lais2(g, **clust_kwargs)
elif (options.method == 'big_clam'):
communities = algorithms.big_clam(g, **clust_kwargs)
elif (options.method == 'danmf'):
communities = algorithms.danmf(g, **clust_kwargs)
elif (options.method == 'ego_networks'):
communities = algorithms.ego_networks(g, **clust_kwargs)
elif (options.method == 'egonet_splitter'):
communities = algorithms.egonet_splitter(g, **clust_kwargs)
elif (options.method == 'nmnf'):
communities = algorithms.nmnf(g, **clust_kwargs)
elif (options.method == 'nnsed'):
communities = algorithms.nnsed(g, **clust_kwargs)
elif (options.method == 'slpa'):
communities = algorithms.slpa(g, **clust_kwargs)
elif (options.method == 'bimlpa'):