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()]
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}
return l
nums = [802, 810, 822, 834, 844, 853, 863, 878, 883, 999]
for num in nums:
reader = GraphReader("twitch%s" % num)
graph = reader.get_graph()
target = reader.get_target()
d = {}
model = LabelPropagation()
comms = run(model, graph)
coeffs = []
for key in sorted(comms.keys()):
sub = graph.subgraph(list(comms[key]))
coeff = nx.average_clustering(sub)
coeffs.append(coeff)
avg_coeff = sum(coeffs) / len(coeffs)
d['label'] = avg_coeff
model = EdMot()
comms = run(model, graph)
coeffs = []
for key in sorted(comms.keys()):
sub = graph.subgraph(list(comms[key]))
coeff = nx.average_clustering(sub)
nums = [802, 810, 822, 834, 844, 853, 863, 878, 883, 999]
algs = ['label', 'edmot', 'gemsec', 'scd']
for alg_name in algs:
path = str(os.getcwd()) + "/scores/" + str(alg_name)
if not os.path.exists(str(path)):
os.makedirs(path)
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
"""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)
