def test_diff2vec():
"""
Testing the Diff2Vec class.
"""
model = Diff2Vec()
graph = nx.watts_strogatz_graph(100, 10, 1.0)
model.fit(graph)
embedding = model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert embedding.shape[1] == model.dimensions
assert type(embedding) == np.ndarray
model = Diff2Vec(dimensions=32)
graph = nx.watts_strogatz_graph(150, 10, 1.0)
model.fit(graph)
embedding = model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert embedding.shape[1] == model.dimensions
assert type(embedding) == np.ndarray
def karate_factory(algo, dim, nwalks, workers):
if algo == "walklets":
karate_obj = Walklets(dimensions=int(dim / 4),
walk_number=nwalks,
workers=workers)
elif algo == "role2vec":
karate_obj = Role2Vec(dimensions=dim,
walk_number=nwalks,
workers=workers)
elif algo == "diff2vec":
karate_obj = Diff2Vec(dimensions=dim,
diffusion_number=nwalks,
workers=workers)
elif algo == "deepwalk":
karate_obj = DeepWalk(dimensions=dim,
walk_number=nwalks,
workers=workers)
elif algo == "boostne":
karate_obj = BoostNE(dimensions=int(dim / 17) + 1)
elif algo == "nodesketch":
karate_obj = NodeSketch(dimensions=dim)
elif algo == "netmf":
karate_obj = NetMF(dimensions=dim)
elif algo == "hope":
karate_obj = HOPE(dimensions=dim)
elif algo == "grarep":
karate_obj = GraRep(dimensions=int(dim / 5) + 1)
elif algo == "nmfadmm":
karate_obj = NMFADMM(dimensions=int(dim / 2))
elif algo == "graphwave":
karate_obj = GraphWave()
elif algo == "laplacian":
karate_obj = LaplacianEigenmaps(dimensions=dim)
else:
raise RuntimeError("Invalid model type: %s" % algo)
return karate_obj