def test_HLC(self):
g = get_string_graph()
coms = algorithms.hierarchical_link_community(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]), tuple)
def test_read_write_json(self):
g = nx.karate_club_graph()
communities = algorithms.louvain(g)
readwrite.write_community_json(communities, "coms.json")
communities_r = readwrite.read_community_json("coms.json")
self.assertListEqual(communities.communities,
communities_r.communities)
os.remove("coms.json")
communities = algorithms.louvain(g)
readwrite.write_community_json(communities, "coms.gzip", zip=True)
communities_r = readwrite.read_community_json("coms.gzip", zip=True)
self.assertListEqual(communities.communities,
communities_r.communities)
os.remove("coms.gzip")
communities = algorithms.frc_fgsn(g, 1, 0.5, 3)
readwrite.write_community_json(communities, "coms.json")
communities_r = readwrite.read_community_json("coms.json")
self.assertListEqual(communities.communities,
communities_r.communities)
os.remove("coms.json")
communities = algorithms.hierarchical_link_community(g)
readwrite.write_community_json(communities, "coms.json")
communities_r = readwrite.read_community_json("coms.json")
self.assertListEqual(communities.communities,
communities_r.communities)
with open("coms.json") as f:
cr = f.read()
readwrite.read_community_from_json_string(cr)
os.remove("coms.json")
def test_node_map(self):
g = nx.karate_club_graph()
coms = algorithms.hierarchical_link_community(g)
edge_com_map = coms.to_edge_community_map()
self.assertIsInstance(edge_com_map, dict)
def test_to_json(self):
g = nx.karate_club_graph()
coms = algorithms.hierarchical_link_community(g)
self.assertIsInstance(coms, EdgeClustering)
js = coms.to_json()
self.assertIsInstance(js, str)
def make_communities(g, method):
'''
Function to run community detection
Inputs:
g : networkx object
Networkx network object representing raw music data
method : string
String identifying clustering method to be used.
Options are (case-sensitive):
1) infomap
2) LPM
3) louvain
4) HLC
Returns:
graph : networkx object
Networkx network object with added community data
'''
print("*******Inside main comm function *******")
if method == "infomap":
edge_tuples = [edge.tuple for edge in g.es]
im = infomap.Infomap()
im.add_links(edge_tuples)
im.run("-d -N 10")
modules = im.get_multilevel_modules()
# igraph non-hierarchical version
#infomap_partition = g.community_infomap(edge_weights='weight')
infomap_partition_assignment = {
g.vs[i]['name']: modules[i]
for i in range(g.vcount())
}
return infomap_partition_assignment
elif method == "LPM":
lpm_partition = g.community_label_propagation(weights='weight')
lpm_partition_assignment = {
g.vs[i]['name']: [lpm_partition.membership[i]]
for i in range(g.vcount())
}
return lpm_partition_assignment
elif method == 'louvain':
louvain_partition = g.community_multilevel(
weights=[e['weight'] for e in g.es], return_levels=True)
louvain_partition_assignment = {
g.vs[i]['name']:
[level.membership[i] for level in louvain_partition]
for i in range(len(g.vs))
}
return louvain_partition_assignment
elif method == 'HLC':
coms = algorithms.hierarchical_link_community(g)
return coms.communities