def exec_struc2vec(args):
'''
Pipeline for representational learning for all nodes in a graph.
'''
if (args.OPT3):
until_layer = args.until_layer
else:
until_layer = None
G = read_graph()
G = struc2vec.Graph(G, args.directed, args.workers, untilLayer=until_layer)
if (args.OPT1):
G.preprocess_neighbors_with_bfs_compact()
else:
G.preprocess_neighbors_with_bfs()
if (args.OPT2):
G.create_vectors()
G.calc_distances(compactDegree=args.OPT1)
else:
G.calc_distances_all_vertices(compactDegree=args.OPT1)
G.create_distances_network()
G.preprocess_parameters_random_walk()
G.simulate_walks(args.num_walks, args.walk_length)
return G
def struc2vec(self):
if self.nodeGraph == '':
return
beginTime = time.time()
print('1: struc2vec Begin')
strucGraph = graph.from_networkx(self.nodeGraph)
strucGraph = struc2vec.Graph(strucGraph,
'undirected',
4,
untilLayer=None)
if True:
strucGraph.preprocess_neighbors_with_bfs_compact()
else:
strucGraph.preprocess_neighbors_with_bfs()
if True:
strucGraph.create_vectors()
strucGraph.calc_distances(compactDegree=True)
else:
strucGraph.calc_distances_all_vertices(compactDegree=True)
strucGraph.create_distances_network()
strucGraph.preprocess_parameters_random_walk()
strucGraph.simulate_walks(10, 80)
walks = LineSentence('random_walks.txt')
self.model = Word2Vec(walks,
size=self.D,
window=10,
min_count=0,
hs=1,
sg=1,
workers=4,
iter=5)
print('Time of Struc2Vec', time.time() - beginTime)
def exec_struc2vec(input_file):
'''
Pipeline for representational learning for all nodes in a graph.
'''
until_layer = 6
G = read_graph(input_file)
G = struc2vec.Graph(G, 'undirected', workers=4, untilLayer=until_layer)
G.preprocess_neighbors_with_bfs_compact()
G.create_vectors()
G.calc_distances(compactDegree=True)
G.create_distances_network()
G.preprocess_parameters_random_walk()
G.simulate_walks(10, 80)
return G
def exec_struc2vec(args):
'''
Pipeline for representational learning for all nodes in a graph.
'''
#K = args.num_walks
#S = args.walk_length
K = 10
S = 5
G = read_graph()
G = struc2vec.Graph(G, args.directed, args.workers, K, S)
G.preprocess_neighbors_with_rw()
G.create_vectors()
G.calc_distances()
G.create_distances_network()
walks = G.simulate_walks(args.num_walks, args.walk_length)
return walks
