def test_danmf():
"""
Test the DANMF procedure.
"""
graph = nx.newman_watts_strogatz_graph(100, 5, 0.3)
model = DANMF()
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
embedding = model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert embedding.shape[1] == 2 * model.layers[-1]
# Edmot example #---------------- g = nx.newman_watts_strogatz_graph(100, 10, 0.9) model = EdMot(3, 0.5) model.fit(g) #---------------- # 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
"""DANMF example.""" import networkx as nx from karateclub.community_detection.overlapping import DANMF g = nx.newman_watts_strogatz_graph(100, 20, 0.05) model = DANMF() model.fit(g)
def danmf(population, neighbors, probability):
g = nx.newman_watts_strogatz_graph(population, neighbors, probability)
model = DANMF()
model.fit(g)
#print(model.get_embedding())
return [model.get_memberships(), model.get_embedding()]