def nnsed(population, neighbors, probability):
g = nx.newman_watts_strogatz_graph(population, neighbors, probability)
model = NNSED()
model.fit(g)
embedding = model.get_embedding()
#print(embedding)
memberships = model.get_memberships()
#print(memberships)
return [memberships]
def test_nnsed():
"""
Test the NNSED procedure.
"""
graph = nx.newman_watts_strogatz_graph(100, 5, 0.3)
model = NNSED()
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] == model.dimensions
model.fit(g) embedding = model.get_embedding() #------------------- # GraphWave example #------------------- g = nx.newman_watts_strogatz_graph(100, 10, 0.02) model = GraphWave() model.fit(g) embedding = model.get_embedding() #--------------- # NNSED example #--------------- g = nx.newman_watts_strogatz_graph(100, 10, 0.02) model = NNSED() model.fit(g) embedding = model.get_embedding() memberships = model.get_memberships()