def test_gn(self):
g = get_string_graph()
coms = algorithms.girvan_newman(g, 3)
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)
def girvan_newman(self):
template = pd.read_csv("data/communities_template.node", " ", header='infer')
saveTo = "Results/Communities/" + self.name + "_girvannewman_communities.node"
G = nx.Graph(self.g)
result = algorithms.girvan_newman(G, level=3)
modularity = result.newman_girvan_modularity().score
significance = result.significance().score
communities = result.to_node_community_map()
n_communities = list(communities.values())[-1][0] + 1
print("\nGirvan Newman: ")
print("#Communities: ", n_communities)
print("Modularity: ", modularity)
print("Significance: ", significance)
e = self.get_ordered_communities(communities)
template['Girvan-Newman'] = e
print("\n")
template['Degree'] = [v for v in self.centrality]
template['RegionName'] = self.region_names
pd.DataFrame.to_csv(template, saveTo, " ", header=False, index=False)
return e
def gn(n_coms) : return lambda G : algorithms.girvan_newman(G, n_coms) algos['gn'] = [gn(len(c)) for c in lfr_comms]
cs.erdos_renyi_modularity().score))
print("{0:>15s} | {1:.6f}".format(
'Robustness',
cs.normalized_mutual_information(alg(G)).score))
print("{0:>15s} | {1:.1f} sec\n".format('Timing', time() - tic))
return cs
# G = read('toy')
G = read('karate')
# G = read('women')
# G = read('dolphins')
# G = read('got-appearance')
# G = read('diseasome')
# G = read('wars')
# G = read('transport')
# G = read('java')
# G = read('imdb')
# G = read('wikileaks')
info(G)
clusters(G, lambda G: algorithms.girvan_newman(G, level=1))
clusters(G, lambda G: algorithms.label_propagation(G))
cs = clusters(G, lambda G: algorithms.louvain(G))
# clusters(G, lambda G: algorithms.leiden(G))
# clusters(G, lambda G: algorithms.sbm_dl(G))
viz.plot_network_clusters(G, cs, nx.spring_layout(G))
plt.show()
