python中用PageRank算法计算acl.txt文本节点的PR值的代码

以下是Python中用PageRank算法计算acl.txt文本节点的PR值的示例代码：

import numpy as np

# 读取acl.txt文本文件并创建图结构
with open('acl.txt') as file:
    lines = file.readlines()

graph = {}
nodes = set()

for line in lines:
    node, neighbor = line.strip().split(' ')
    nodes.add(node)
    nodes.add(neighbor)
    if node not in graph:
        graph[node] = []
    graph[node].append(neighbor)

# 初始化PageRank值和转移矩阵
n = len(nodes)
pagerank = np.ones(n) / n
transition_matrix = np.zeros((n, n))

# 构建转移矩阵
for i, node in enumerate(sorted(nodes)):
    if node in graph:
        neighbors = graph[node]
        for neighbor in neighbors:
            j = sorted(nodes).index(neighbor)
            transition_matrix[j, i] = 1 / len(neighbors)

# 迭代计算PageRank值
d = 0.85 # damping factor
for _ in range(100):
    pagerank_new = (1 - d) / n + d * transition_matrix.dot(pagerank)
    if abs(pagerank_new - pagerank).max() < 1e-6:
        break
    pagerank = pagerank_new

# 输出节点PR值
for i, node in enumerate(sorted(nodes)):
    print('{}: {:.4f}'.format(node, pagerank[i]))

这段代码使用Python的numpy模块计算PageRank值，并将结果输出到终端。为了保证隐私安全，代码中没有保存或打印任何用户信息。