def test_label_propagation():
"""
Test Label Propagation procedure.
"""
graph = nx.newman_watts_strogatz_graph(50, 5, 0.3)
model = LabelPropagation()
model.fit(graph)
memberships = model.get_memberships()
indices = [k for k, v in memberships.items()].sort()
nodes = [node for node in graph.nodes()].sort()
assert graph.number_of_nodes() == len(memberships)
assert indices == nodes
assert type(memberships) == dict
graph = nx.newman_watts_strogatz_graph(150, 5, 0.3)
model = LabelPropagation()
model.fit(graph)
memberships = model.get_memberships()
indices = [k for k, v in memberships.items()].sort()
nodes = [node for node in graph.nodes()].sort()
assert graph.number_of_nodes() == len(memberships)
assert indices == nodes
assert type(memberships) == dict
def labelPropagation(population, neighbors, probability):
g = nx.newman_watts_strogatz_graph(population, neighbors, probability)
model = LabelPropagation()
model.fit(g)
#print(model.get_memberships())
return [model.get_memberships()]
for num in nums:
file = open(
str(path) + str(os.sep) + str(alg_name) + "_" + str(num) + ".csv",
"w")
csvwriter = csv.writer(file)
csvwriter.writerow(['comm', 'size'])
reader = GraphReader("twitch%s" % num)
graph = reader.get_graph()
if alg_name == 'label':
model = LabelPropagation()
elif alg_name == 'edmot':
model = EdMot()
elif alg_name == 'gemsec':
model = GEMSEC()
elif alg_name == 'scd':
model = SCD()
l = {}
model.fit(graph)
cluster_membership = model.get_memberships()
for key in sorted(cluster_membership.keys()):
try:
l[cluster_membership[key]].add(key)
except:
l[cluster_membership[key]] = {key}
tot = 0
for key in sorted(l.keys()):
csvwriter.writerow([key, len(l[key])])
tot += len(l[key])
print(tot)
file.flush()
file.close()
"""Label Propagation example.""" import networkx as nx from karateclub.community_detection.non_overlapping import LabelPropagation g = nx.newman_watts_strogatz_graph(100, 20, 0.05) model = LabelPropagation() model.fit(g)
