def test_mnmf():
"""
Test the MNMF procedure.
"""
graph = nx.newman_watts_strogatz_graph(100, 5, 0.3)
model = MNMF()
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
embedding = model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert embedding.shape[1] == model.dimensions
assert type(embedding) == np.ndarray
centers = model.get_cluster_centers()
assert centers.shape[0] == model.clusters
assert centers.shape[1] == model.dimensions
assert type(centers) == np.ndarray
def mnmf(population, neighbors, probability):
g = nx.newman_watts_strogatz_graph(population, neighbors, probability)
model = MNMF()
model.fit(g)
#print(model.get_memberships())
#print(model.get_embedding())
#print(model.get_cluster_centers())
return [
model.get_memberships(),
model.get_embedding(),
model.get_cluster_centers()
]
# DANMF example #---------------- g = nx.newman_watts_strogatz_graph(100, 10, 0.02) model = DANMF() model.fit(g) #---------------- # M-NMF example #---------------- g = nx.newman_watts_strogatz_graph(100, 10, 0.02) model = MNMF() model.fit(g) #---------------------------- # Label Propagation example #---------------------------- g = nx.newman_watts_strogatz_graph(100, 10, 0.02) model = LabelPropagation() model.fit(g) #----------------- # GraRep example
"""MNMF example.""" import networkx as nx from karateclub.community_detection.overlapping import MNMF g = nx.newman_watts_strogatz_graph(100, 20, 0.05) model = MNMF() model.fit(g)