链路预测代码python

链路预测是指在一个网络中，预测两个节点之间是否存在连接。以下是一个基于Python的链路预测代码示例：

import networkx as nx
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import scale
from sklearn.linear_model import LogisticRegression

# 读取数据
G = nx.read_edgelist('path/to/edge/list', nodetype=int)

# 拆分数据集
edgelist = list(G.edges())
np.random.shuffle(edgelist)
train_edges = edgelist[:int(0.8 * len(edgelist))]
test_edges = edgelist[int(0.8 * len(edgelist)):]

# 创建负样本集
all_nodes = set(G.nodes())
non_edges = set()
for i in all_nodes:
    for j in all_nodes:
        if i != j and (i, j) not in G.edges():
            non_edges.add((i, j))
non_edges = list(non_edges)
np.random.shuffle(non_edges)
train_non_edges = non_edges[:int(0.8 * len(non_edges))]
test_non_edges = non_edges[int(0.8 * len(non_edges)):]

# 创建训练集和测试集
train = [(x[0], x[1], 1) for x in train_edges] + [(x[0], x[1], 0) for x in train_non_edges]
test = [(x[0], x[1], 1) for x in test_edges] + [(x[0], x[1], 0) for x in test_non_edges]

# 计算图形特征
# 创建一个字典来存储特征向量
features = {}
for i in all_nodes:
    features[i] = []

# 计算度 centrality
for i in all_nodes:
    features[i].append(G.degree(i))

# 计算聚集系数 clustering coefficient
for i in all_nodes:
    features[i].append(nx.clustering(G, i))

# 计算 PageRank
pr = nx.pagerank(G)
for i in all_nodes:
    features[i].append(pr[i])

# 训练模型
X_train = np.array([features[x[0]] + features[x[1]] for x in train])
y_train = np.array([x[2] for x in train])
X_train = scale(X_train)
clf = LogisticRegression()
clf.fit(X_train, y_train)

# 测试模型
X_test = np.array([features[x[0]] + features[x[1]] for x in test])
y_test = np.array([x[2] for x in test])
X_test = scale(X_test)
y_pred = clf.predict_proba(X_test)[:, 1]
print("ROC AUC score: ", roc_auc_score(y_test, y_pred))

这个示例代码使用了 `networkx` 库来读取边列表和计算图形特征。它还使用了 `sklearn` 库来训练逻辑回归模型，并使用 ROC AUC 分数来评估模型的预测性能。在这个示例中，我们使用了度中心性、聚集系数和 PageRank 作为图形特征。