运用DeepWalk 对图进行嵌入并使用逻辑回归进行链路预测的 Python 代码
时间: 2023-07-19 08:42:08 浏览: 117
以下是使用DeepWalk对图进行嵌入并使用逻辑回归进行链路预测的Python代码:
```python
import numpy as np
import networkx as nx
from gensim.models import Word2Vec
from sklearn.linear_model import LogisticRegression
# 构建图
G = nx.read_edgelist('edge_list.txt', nodetype=int)
# 使用DeepWalk嵌入图
walks = []
for node in G.nodes():
for i in range(5):
walk = list(map(str, nx.random_walk(G, node)))
walks.append(walk)
model = Word2Vec(walks, size=128, window=5, min_count=0, sg=1, workers=4)
# 构建训练数据和标签
edges = np.array(G.edges())
labels = np.array([1] * len(edges))
neg_edges = []
for i in range(len(edges)):
src, dst = edges[i]
neg_node = np.random.choice(list(G.nodes()))
while G.has_edge(src, neg_node) or G.has_edge(dst, neg_node):
neg_node = np.random.choice(list(G.nodes()))
neg_edges.append([src, neg_node])
neg_edges.append([dst, neg_node])
neg_edges = np.array(neg_edges)
neg_labels = np.array([0] * len(neg_edges))
X = np.concatenate((edges, neg_edges))
y = np.concatenate((labels, neg_labels))
# 训练逻辑回归模型
lr = LogisticRegression()
lr.fit([model[str(src)] + model[str(dst)] for src, dst in edges], labels)
# 预测新的链路
new_edges = [[1, 2], [3, 4], [5, 6]]
predict_prob = lr.predict_proba([model[str(src)] + model[str(dst)] for src, dst in new_edges])
print(predict_prob[:, 1])
```
这个代码假设已经有一个由边列表表示的图,该列表存储在名为`edge_list.txt`的文件中,每行包含两个整数,表示一条边。该代码首先使用DeepWalk嵌入图,然后使用嵌入向量训练逻辑回归模型,最后预测新链路的概率。
阅读全文