] not in edges_temp and edges[j][1] != edges[j][0]:
edges_temp.append(edges[j])
# 找到节点数
for z in edges[j]:
node_num = max(node_num, z)
edges_list.append(edges_temp)
node_num += 1
for edges in edges_list:
graph = nx.DiGraph()
graph.add_nodes_from([i for i in range(node_num)])
graph.add_edges_from(edges)
graphs.append(graph)
if 'enron_large' in basepath:
model = DeepWalk(graph,
walk_length=10,
num_walks=80,
workers=1)
model.train(window_size=5, iter=3)
else:
model = DeepWalk(graph,
walk_length=10,
num_walks=800,
workers=1)
model.train(window_size=5, iter=3)
embeddings = model.get_embeddings()
embeddings_list.append(embeddings)
pred_edges = []
emb_matrix = np.zeros([node_num, 128])
for node, emb in embeddings.items():
node = int(node)
emb = emb.reshape((1, emb.shape[0]))
def get_embeddings(self, inst, th=1):
G = nx.parse_edgelist(self._compose_edge_list(inst.dist_mat, th), create_using=nx.DiGraph(), nodetype=None,
data=[('weight', float)])
if self._embedding == 'deepwalk':
model = DeepWalk(G, walk_length=10, num_walks=80, workers=1)
model.train(window_size=5, iter=3)
elif self._embedding == 'node2vec':
model = Node2Vec(G, walk_length=10, num_walks=80, p=0.25, q=4, workers=1) # init model
model.train(window_size=5, iter=3) # train model
elif self._embedding == 'line':
model = LINE(G, embedding_size=128, order='second') # init model,order can be ['first','second','all']
model.train(batch_size=1024, epochs=50, verbose=2) # train model
elif self._embedding == 'sdne':
model = SDNE(G, hidden_size=[256, 128]) # init model
model.train(batch_size=3000, epochs=40, verbose=2) # train model
elif self._embedding == 'struc2vec':
model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) # init model
model.train(window_size=5, iter=3) # train model
else:
return self._normalise(inst)
ebds = model.get_embeddings()
coords = []
for i in range(inst.n):
coords.append(ebds[str(i)])
return np.array(coords)