写一段nodevec的代码
时间: 2024-02-12 20:08:20 浏览: 19
node2vec算法的实现需要对图进行随机游走和节点嵌入向量的训练,下面是一个简单的Python实现:
```python
import numpy as np
import networkx as nx
from gensim.models import Word2Vec
class Node2Vec:
def __init__(self, graph, dimensions=128, walk_length=80, num_walks=10, p=1, q=1, window_size=10):
self.graph = graph
self.dimensions = dimensions
self.walk_length = walk_length
self.num_walks = num_walks
self.p = p
self.q = q
self.window_size = window_size
def simulate_walks(self, num_walks, walk_length, p, q):
G = self.graph
walks = []
nodes = list(G.nodes())
for n_walk in range(num_walks):
np.random.shuffle(nodes)
for node in nodes:
walks.append(self.node2vec_walk(walk_length=walk_length, start_node=node, p=p, q=q))
return walks
def node2vec_walk(self, walk_length, start_node, p, q):
G = self.graph
walk = [start_node]
while len(walk) < walk_length:
curr_node = walk[-1]
neighbors = list(G.neighbors(curr_node))
if len(neighbors) > 0:
if len(walk) == 1:
walk.append(np.random.choice(neighbors))
else:
prev_node = walk[-2]
weights = [self.transition_probability(curr_node, neighbor, prev_node, p, q) for neighbor in neighbors]
weights = np.array(weights)
weights /= np.sum(weights)
walk.append(np.random.choice(neighbors, p=weights))
else:
break
return walk
def transition_probability(self, curr_node, neighbor, prev_node, p, q):
G = self.graph
pi = 1
if prev_node in list(G.neighbors(curr_node)):
if neighbor == prev_node:
pi = 1 / p
else:
pi = 1
else:
if neighbor == prev_node:
pi = 1
else:
pi = 1 / q
return pi
def learn_embeddings(self, walks):
walks = [list(map(str, walk)) for walk in walks]
model = Word2Vec(walks, size=self.dimensions, window=self.window_size, min_count=0, sg=1, workers=8, iter=1)
return model.wv
```
使用示例:
```python
# 创建一个图对象
G = nx.karate_club_graph()
# 创建一个Node2Vec对象
node2vec = Node2Vec(G, dimensions=64, walk_length=30, num_walks=200, p=1, q=1, window_size=10)
# 生成随机游走序列
walks = node2vec.simulate_walks(node2vec.num_walks, node2vec.walk_length, node2vec.p, node2vec.q)
# 训练节点嵌入向量
embeddings = node2vec.learn_embeddings(walks)
```