GraphEmbedding
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关于deepwalk的随机采样问题
def deepwalk_walk(self, walk_length, 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:
walk.append(random.choice(cur_nbrs))
else:
break
return walk
个人觉得上面的函数是否不太妥当,完全没用到,各个结点间的转移概率。各个节点间的转移概率其实是可以统计得到的,是否用上会更好?