def test_DER(self):
g = get_string_graph()
coms = algorithms.der(g)
self.assertEqual(type(coms.communities), list)
if len(coms.communities) > 0:
self.assertEqual(type(coms.communities[0]), list)
self.assertEqual(type(coms.communities[0][0]), str)
fields = line.strip("\n").split("\t")
if (options.bipartite):
g.add_node(fields[0], bipartite=0)
g.add_node(fields[1], bipartite=1)
g.add_edge(fields[0], fields[1], weight=float(fields[2]))
print(g.number_of_nodes())
print(g.number_of_edges())
if (options.method == 'leiden'):
communities = algorithms.leiden(g, weights='weight', **clust_kwargs)
elif (options.method == 'louvain'):
communities = algorithms.louvain(g, weight='weight', **clust_kwargs)
elif (options.method == 'cpm'):
communities = algorithms.cpm(g, weights='weight', **clust_kwargs)
elif (options.method == 'der'):
communities = algorithms.der(g, **clust_kwargs)
elif (options.method == 'edmot'):
communities = algorithms.edmot(g, **clust_kwargs)
elif (options.method == 'eigenvector'):
communities = algorithms.eigenvector(g, **clust_kwargs)
elif (options.method == 'gdmp2'):
communities = algorithms.gdmp2(g, **clust_kwargs)
elif (options.method == 'greedy_modularity'):
communities = algorithms.greedy_modularity(g,
weight='weight',
**clust_kwargs)
#elif(options.method == 'infomap'):
# communities = algorithms.infomap(g)
elif (options.method == 'label_propagation'):
communities = algorithms.label_propagation(g, **clust_kwargs)
elif (options.method == 'markov_clustering'):