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View Code? Open in Web Editor NEWThis is an implementation of LINE(Large-scale information network embedding) algorithm.
This is an implementation of LINE(Large-scale information network embedding) algorithm.
class LINE(object):
def __init__(self, graph_edge_num, graph_nodes_num,
dimension):
self.e = graph_edge_num
self.n = graph_nodes_num
self.steps_per_epoch = None
self.epoch_train_size = None
self.dimension = dimension
def _generate_batch_train(self, adj_list,
graph_nodes_num, graph_edge_num,
batch_size, negativeRatio,
negative_sampling):
# 使用 negative sampling 优化
table_size = 1e8
power = 0.75
sampling_table = None
data = np.ones((adj_list.shape[0]), dtype=np.int8)
mat = csr_matrix((data, (adj_list[:, 0], adj_list[:, 1])), shape=(graph_nodes_num, graph_nodes_num),
dtype=np.int8)
batch_size_ones = np.ones((batch_size), dtype=np.int8)
nb_train_sample = adj_list.shape[0]
index_array = np.arange(nb_train_sample)
nb_batch = int(np.ceil(nb_train_sample / float(batch_size)))
batches = [(i * batch_size, min(nb_train_sample, (i + 1) * batch_size)) for i in range(0, nb_batch)]
if negative_sampling == "NON-UNIFORM":
print("Pre-procesing for non-uniform negative sampling!")
node_degree = np.zeros(graph_nodes_num)
for i in range(graph_edge_num):
node_degree[adj_list[i, 0]] += 1
node_degree[adj_list[i, 1]] += 1
norm = sum([math.pow(node_degree[i], power) for i in range(graph_nodes_num)])
sampling_table = np.zeros(int(table_size), dtype=np.uint32)
p = 0
i = 0
for j in range(graph_nodes_num):
p += float(math.pow(node_degree[j], power)) / norm
while i < table_size and float(i) / table_size < p:
sampling_table[i] = j
i += 1
while 1:
for batch_index, (batch_start, batch_end) in enumerate(batches):
pos_edge_list = index_array[batch_start:batch_end]
pos_left_nodes = adj_list[pos_edge_list, 0]
pos_right_nodes = adj_list[pos_edge_list, 1]
pos_relation_y = batch_size_ones[0:len(pos_edge_list)]
neg_left_nodes = np.zeros(len(pos_edge_list) * negativeRatio, dtype=np.int32)
neg_right_nodes = np.zeros(len(pos_edge_list) * negativeRatio, dtype=np.int32)
neg_relation_y = np.zeros(len(pos_edge_list) * negativeRatio, dtype=np.int8)
h = 0
for i in pos_left_nodes:
for k in range(negativeRatio):
rn = sampling_table[random.randint(0,
table_size - 1)] if negative_sampling == "NON-UNIFORM" else random.randint(
0, graph_nodes_num - 1)
while mat[i, rn] == 1 or i == rn:
rn = sampling_table[random.randint(0,
table_size - 1)] if negative_sampling == "NON-UNIFORM" else random.randint(
0, graph_nodes_num - 1)
neg_left_nodes[h] = i
neg_right_nodes[h] = rn
h += 1
left_nodes = np.concatenate((pos_left_nodes, neg_left_nodes), axis=0)
right_nodes = np.concatenate((pos_right_nodes, neg_right_nodes), axis=0)
relation_y = np.concatenate((pos_relation_y, neg_relation_y), axis=0)
yield ([left_nodes, right_nodes], [relation_y])
def _model(self, graph_nodes_num, dimension):
left_input = Input(shape=(1,))
right_input = Input(shape=(1,))
left_model = Sequential()
left_model.add(Embedding(input_dim=graph_nodes_num + 1, output_dim=dimension, input_length=1, mask_zero=False))
left_model.add(Reshape((dimension,)))
right_model = Sequential()
right_model.add(Embedding(input_dim=graph_nodes_num + 1, output_dim=dimension, input_length=1, mask_zero=False))
right_model.add(Reshape((dimension,)))
left_embed = left_model(left_input)
right_embed = left_model(right_input)
left_right_dot = dot(inputs=[left_embed, right_embed], axes=1, name="left_right_dot")
model = Model(inputs=[left_input, right_input], outputs=[left_right_dot])
embed_generator = Model(inputs=[left_input, right_input], outputs=[left_embed, right_embed])
return model, embed_generator
def _line_loss(self, y_true, y_pred):
coeff = y_true*2 - 1
return -K.mean(K.log(K.sigmoid(coeff*y_pred)))
def fit(self, adj_list, batch_size, negative_ratio, negative_sampling, epoch_num):
self.steps_per_epoch = int(self.e / batch_size)
self.epoch_train_size = (1 + negative_ratio) * self.e
# 产生训练样本
data = self._generate_batch_train(adj_list, self.n, self.e, batch_size, negative_ratio,
negative_sampling)
self.model, self.embed_generator = self._model(self.n, self.dimension)
# model.summary()
self.model.compile(optimizer='rmsprop', loss={'left_right_dot': self._line_loss})
self.model.fit_generator(data, steps_per_epoch=self.epoch_train_size / batch_size, epochs=epoch_num, verbose=1)
def predict(self, data):
# link prediction
return self.embed_generator.predict_on_batch(data)
line = LINE(graph_edge_num, graph_nodes_num, dimension)
line.fit(adj_list, batch_size, negative_ratio, negative_sampling, epoch_num)
Hi there,
I can run your code successfully, but it is extremely slow. For example, it will take more than 5 days in order to get the DBLP result. How can I setup GPU to speed up the process?
Many Thanks,
Long
I don't understand the implementation very well. It seems that the loss function is quite different from what given in the paper. Also, the paper gives two methods, 1st order and 2rd order, is this implementation just include the first order method.
I'm an undergraduate student new in this field. Hope to get some instructions.
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