import numpy as np
import networkx as nx
from gensim.models import Word2Vec, KeyedVectors
import random
from .. import BaseModel, register_model
[docs]@register_model("deepwalk")
class DeepWalk(BaseModel):
r"""The DeepWalk model from the `"DeepWalk: Online Learning of Social Representations"
<https://arxiv.org/abs/1403.6652>`_ paper
Args:
hidden_size (int) : The dimension of node representation.
walk_length (int) : The walk length.
walk_num (int) : The number of walks to sample for each node.
window_size (int) : The actual context size which is considered in language model.
worker (int) : The number of workers for word2vec.
iteration (int) : The number of training iteration in word2vec.
"""
@staticmethod
[docs] def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--walk-length', type=int, default=80,
help='Length of walk per source. Default is 80.')
parser.add_argument('--walk-num', type=int, default=40,
help='Number of walks per source. Default is 40.')
parser.add_argument('--window-size', type=int, default=5,
help='Window size of skip-gram model. Default is 5.')
parser.add_argument('--worker', type=int, default=10,
help='Number of parallel workers. Default is 10.')
parser.add_argument('--iteration', type=int, default=10,
help='Number of iterations. Default is 10.')
# fmt: on
@classmethod
[docs] def build_model_from_args(cls, args):
return cls(
args.hidden_size,
args.walk_length,
args.walk_num,
args.window_size,
args.worker,
args.iteration,
)
def __init__(
self, dimension, walk_length, walk_num, window_size, worker, iteration
):
super(DeepWalk, self).__init__()
self.dimension = dimension
self.walk_length = walk_length
self.walk_num = walk_num
self.window_size = window_size
self.worker = worker
self.iteration = iteration
[docs] def train(self, G):
self.G = G
walks = self._simulate_walks(self.walk_length, self.walk_num)
walks = [[str(node) for node in walk] for walk in walks]
model = Word2Vec(
walks,
size=self.dimension,
window=self.window_size,
min_count=0,
sg=1,
workers=self.worker,
iter=self.iteration,
)
id2node = dict([(vid, node) for vid, node in enumerate(G.nodes())])
embeddings = np.asarray([model.wv[str(id2node[i])] for i in range(len(id2node))])
return embeddings
[docs] def _walk(self, start_node, walk_length):
# Simulate a random walk starting from start node.
walk = [start_node]
while len(walk) < walk_length:
cur = walk[-1]
cur_nbrs = list(self.G.neighbors(cur))
if len(cur_nbrs) == 0:
break
k = int(np.floor(np.random.rand() * len(cur_nbrs)))
walk.append(cur_nbrs[k])
return walk
[docs] def _simulate_walks(self, walk_length, num_walks):
# Repeatedly simulate random walks from each node.
G = self.G
walks = []
nodes = list(G.nodes())
print("node number:", len(nodes))
for walk_iter in range(num_walks):
print(str(walk_iter + 1), "/", str(num_walks))
random.shuffle(nodes)
for node in nodes:
walks.append(self._walk(node, walk_length))
return walks