不使用networks函数用python实现betweenness_centrality

可以使用networkx库中的betweenness_centrality函数来计算网络中每个节点的介数中心性。如果你不想使用该函数，可以按照以下步骤来编写代码：

1. 导入所需的库：import networkx as nx

2. 定义一个函数，该函数接收一个图的对象作为输入，并返回每个节点的介数中心性。

def betweenness_centrality(graph):
result = {}
nodes = graph.nodes()
for node in nodes:
result[node] = 0
for source in nodes:
stack = []
pred = {}
dist = {}
sigma = {}
dist[source] = 0
sigma[source] = 1
queue = [source]
for node in nodes:
pred[node] = []
while queue:
current_node = queue.pop(0)
stack.append(current_node)
for neighbor_node in graph.neighbors(current_node):
if neighbor_node not in dist:
queue.append(neighbor_node)
dist[neighbor_node] = dist[current_node] + 1
if dist[neighbor_node] == dist[current_node] + 1:
sigma[neighbor_node] += sigma[current_node]
pred[neighbor_node].append(current_node)
delta = {node: 0 for node in nodes}
while stack:
current_node = stack.pop()
for predecessor_node in pred[current_node]:
delta[predecessor_node] += (sigma[predecessor_node] / sigma[current_node]) * (1 + delta[current_node])
if current_node != source:
result[current_node] += delta[current_node]
return result

3. 调用该函数，并将图对象作为输入。例如：

G = nx.karate_club_graph()
centrality = betweenness_centrality(G)

这将计算karate club network中每个节点的介数中心性，并将其存储在centrality字典中。你可以使用类似命令“print(centrality)”查看结果。