我试图随机化一个网络x图的链接权重来改变网络的介数中心性。我已经验证了权重确实改变了，但它并没有改变edge_betweeness_centrality函数的输出。我怀疑这与向节点和边添加对象有关，因为只要我没有这些就能让它工作。然而，我的代码的其他部分需要这些对象，因此不能删除它们。
我试过load_centrality和betweenness_centrality，都给予了同样的问题。我还验证了我的输入是整数还是浮点数。我知道函数会将值解释为距离，所以我试过普通权重和权重的倒数。我已经在这个问题上纠结了好几周了，希望能得到任何帮助或想法。谢谢。
下面是我的代码。

import networkx as nx
import numpy as np 
from itertools import combinations, groupby
import random 

class Link(object):
  def __init__(self,link_id,rate,distance,network):
        self.link_id = link_id 
        self.rate = rate
        self.distance = distance
        self.network = network
        self.buffer = [] 

class Node:
    def __init__(self,node_id,network): 
        self.node_id = node_id
        self.len = 0   
        self.queue = []
        self.network = network
        self.packet_delay = []
        self.packets = []

def gnp_random_connected_graph(n, p):
        graph_seed = np.random.default_rng(2021)

        network = nx.Graph()
        
        node_dict = {}
        for node in range(n):
            new_node = Node(node, network)
            node_dict[node] = new_node
            

        network.add_nodes_from(node_dict.items())

        edges = list(combinations(range(n), 2))
        link_dict = {}
        for id, node_edges in groupby(edges, key=lambda x: x[0]): 
            rate = random.randint(8000000,40000000) #bps
            distance = random.randint(10,185) #meters
            node_edges = list(node_edges) 
            random_edge = tuple(graph_seed.choice(node_edges))
            link_dict[random_edge] = Link(random_edge,rate,distance,network)
        
        for e in node_edges:  
            if graph_seed.random() < p: 
                link_dict[e] = Link(e,rate,distance,network)

        for key,value in link_dict.items(): 
            nodes = list(network.nodes)
            network.add_edge(nodes[key[0]],nodes[key[1]],obj=value,weight=int(value.distance),rp = float(1/(value.distance)))
            
        return network        

def randomize_weights(network):

            for edge in network.edges():
                rand_num = random.randint(10,100) 
                network[edge[0]][edge[1]]['weight'] = rand_num
                network[edge[0]][edge[1]]['rp'] = 1/rand_num

            return network  

org_net = gnp_random_connected_graph(10,.1)
#print("edges weight",nx.get_edge_attributes(org_net,'weight'))
bc_dict = nx.edge_betweenness_centrality(org_net, weight='rp')
print("OLD",bc_dict)
new_net = randomize_weights(org_net.copy())
#print("edges weight",nx.get_edge_attributes(new_net,'weight'))
new_bc_dict = nx.edge_betweenness_centrality(new_net, weight='rp')
print("NEW", new_bc_dict)