def test_deepwalk():
"""
Testing the DeepWalk class.
"""
model = DeepWalk()
graph = nx.watts_strogatz_graph(100, 10, 0.5)
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 = DeepWalk(dimensions=32)
graph = nx.watts_strogatz_graph(150, 10, 0.5)
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 embed(self):
seed = np.random.randint(1e6)
model = DW(dimensions=self.dim_embedding,
walk_length=self.walk_length,
window_size=self.window_size,
seed=seed)
model.fit(self.graph.to_undirected())
self.embedding = model.get_embedding()
def test_neu():
"""
Test the NEU meta embedding class.
"""
graph = nx.newman_watts_strogatz_graph(100, 20, 0.05)
model = NetMF()
meta_model = NEU()
meta_model.fit(graph, model)
embedding = meta_model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert type(embedding) == np.ndarray
graph = nx.newman_watts_strogatz_graph(200, 20, 0.05)
model = DeepWalk()
meta_model = NEU()
meta_model.fit(graph, model)
embedding = meta_model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
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
def deepwalk_embedding(Graph):
dw = DeepWalk(dimensions=256)
G = nx.convert_node_labels_to_integers(Graph)
dw.fit(G)
embeddings = dw.get_embedding()
return embeddings
if save == 'T':
plt.savefig(output_png_filename)
else:
plt.show()
plt.close()
return
####################################################################################
# Main
####################################################################################
#
# DeepWalk
g = nx.newman_watts_strogatz_graph(100, 20, 0.05)
draw_graph(g)
model = DeepWalk()
model.fit(g)
embedding = model.get_embedding()
#
# Walklets
model = Walklets()
model.fit(g)
embedding = model.get_embedding()
#
# Community Detection with LabelPropagation
# Get graph
reader = GraphReader("facebook")
graph = reader.get_graph()
draw_graph(graph, 'F', 'F', '')
target = reader.get_target()
# Fit