def test_netlsd():
"""
Test the NetLSD embedding.
"""
graphs = [nx.newman_watts_strogatz_graph(50, 5, 0.3) for _ in range(100)]
model = NetLSD()
model.fit(graphs)
embedding = model.get_embedding()
assert embedding.shape[0] == len(graphs)
assert embedding.shape[1] == model.scale_steps
assert type(embedding) == np.ndarray
graphs = [nx.newman_watts_strogatz_graph(500, 5, 0.3) for _ in range(100)]
model = NetLSD()
model.fit(graphs)
embedding = model.get_embedding()
assert embedding.shape[0] == len(graphs)
assert embedding.shape[1] == model.scale_steps
assert type(embedding) == np.ndarray
g = nx.newman_watts_strogatz_graph(1000, 20, 0.05)
model = NetMF()
meta_model = NEU()
meta_model.fit(g, model)
#-----------------------------------
# NetLSD example
#-----------------------------------
graphs = [nx.newman_watts_strogatz_graph(50, 5, 0.3) for _ in range(100)]
model = NetLSD()
model.fit(graphs)
model.get_embedding()
#------------------------------------
# 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)
"""NetLSD illustrative example.""" import networkx as nx from karateclub.graph_embedding import NetLSD graphs = [nx.newman_watts_strogatz_graph(50, 5, 0.3) for _ in range(100)] model = NetLSD() model.fit(graphs) model.get_embedding()