def test_edmot():
"""
Test the EdMot procedure.
"""
graph = nx.newman_watts_strogatz_graph(50, 5, 0.3)
model = EdMot()
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 = EdMot()
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 edmot(population, neighbors, probability):
g = nx.newman_watts_strogatz_graph(population, neighbors, probability)
model = EdMot(3, 0.5)
model.fit(g)
#print(model.get_memberships())
return [model.get_memberships()]
"""EdMot example.""" import networkx as nx from karateclub.community_detection.non_overlapping import EdMot g = nx.newman_watts_strogatz_graph(100, 20, 0.05) model = EdMot() model.fit(g)
from karateclub.community_detection.overlapping import EgoNetSplitter, NNSED, DANMF, MNMF, BigClam, SymmNMF
from karateclub.community_detection.non_overlapping import EdMot, LabelPropagation, SCD
from karateclub.graph_embedding import Graph2Vec, FGSD, GL2Vec, SF
from karateclub.node_embedding.attributed import BANE, TENE, TADW, FSCNMF, SINE, MUSAE
from karateclub.node_embedding.structural import GraphWave, Role2Vec
from karateclub.dataset import GraphReader, GraphSetReader
#------------------------------------
# Edmot example
#------------------------------------
g = nx.newman_watts_strogatz_graph(100, 10, 0.1)
model = EdMot(3, 4)
model.fit(g)
#------------------------------------
# MUSAE example
#------------------------------------
g = nx.newman_watts_strogatz_graph(100, 10, 0.2)
X = {i: random.sample(range(150), 50) for i in range(100)}
row = np.array([k for k, v in X.items() for val in v])
col = np.array([val for k, v in X.items() for val in v])
data = np.ones(100 * 50)
shape = (100, 150)
X = coo_matrix((data, (row, col)), shape=shape)
