怎么用deepwalk做网络重建
时间: 2023-07-12 11:43:12 浏览: 164
DeepWalk是一种常用的图嵌入算法,可以将网络中的节点转化为低维向量,从而方便进行后续的网络分析任务,如节点分类、链路预测等。网络重建是指通过已知的网络结构信息,预测缺失边的存在性,也可以使用DeepWalk进行重建。以下是使用DeepWalk进行网络重建的示例代码:
首先,安装依赖库:
```python
!pip install networkx
!pip install gensim
!pip install scikit-learn
```
然后,导入必要的库:
```python
import networkx as nx
import numpy as np
from gensim.models import Word2Vec
from sklearn.metrics import roc_auc_score, average_precision_score
from sklearn.model_selection import train_test_split
```
接下来,定义函数进行图嵌入:
```python
def graph_embedding(G, dimensions=64, walk_length=30, num_walks=200, workers=4):
walks = []
for node in G.nodes():
for i in range(num_walks):
walks.append(nx.random_walk(G, node, walk_length=walk_length))
embeddings = Word2Vec(walks, size=dimensions, window=10, min_count=0, sg=1, workers=workers)
return embeddings
```
然后,定义函数进行网络重建:
```python
def network_reconstruction(embeddings, edges, test_edges, threshold):
X_train = np.array([np.concatenate((embeddings[str(edge[0])], embeddings[str(edge[1])])) for edge in edges])
y_train = np.array([1 for edge in edges])
X_test = np.array([np.concatenate((embeddings[str(edge[0])], embeddings[str(edge[1])])) for edge in test_edges])
y_test = np.array([1 if edge[2] == 1 else 0 for edge in test_edges])
clf = LogisticRegression(random_state=0)
clf.fit(X_train, y_train)
y_pred = clf.predict_proba(X_test)[:, 1]
y_pred_bin = np.array([1 if pred >= threshold else 0 for pred in y_pred])
roc = roc_auc_score(y_test, y_pred)
ap = average_precision_score(y_test, y_pred)
return roc, ap, y_pred_bin
```
最后,使用示例:
```python
# 构建图
G = nx.karate_club_graph()
# 构建部分边列表和完整边列表
edges = list(G.edges(data=True))
test_edges = []
for node1 in G.nodes():
for node2 in G.nodes():
if node1 != node2 and not G.has_edge(node1, node2):
test_edges.append((node1, node2, 0))
test_edges = test_edges[:100] # 取前100个测试边
# 进行图嵌入
embeddings = graph_embedding(G)
# 进行网络重建
roc, ap, y_pred_bin = network_reconstruction(embeddings, edges, test_edges, threshold=0.5)
# 输出结果
print('ROC AUC score:', roc)
print('Average Precision score:', ap)
print('Predicted edges:', y_pred_bin)
```
代码说明:
该示例使用了karate_club_graph构建了一个简单的图,并将其拆分成部分边列表和完整边列表。然后,使用graph_embedding函数进行图嵌入,生成节点的向量表示。接着,使用network_reconstruction函数进行网络重建,其中使用了逻辑回归模型,根据预测概率的阈值进行二分类。最终,输出ROC AUC和Average Precision两个评价指标的得分,以及预测的边的存在情况。
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