写一段python代码重新实现nx.betweenness_centrality函数

好的，以下是重新实现 nx.betweenness_centrality 函数的 Python 代码：

def my_betweenness_centrality(G):
    betweenness = dict.fromkeys(G, 0.0)
    nodes = G
    n = len(nodes)

    for s in nodes:
        # single source shortest paths
        S = []
        P = {}
        for v in nodes:
            P[v] = []
        sigma = dict.fromkeys(nodes, 0.0)
        D = {}
        sigma[s] = 1.0
        D[s] = 0
        Q = [s]
        while Q:
            v = Q.pop(0)
            S.append(v)
            Dv = D[v]
            sigmav = sigma[v]
            for w in G[v]:
                if w not in D:
                    Q.append(w)
                    D[w] = Dv + 1
                if D[w] == Dv + 1:
                    sigma[w] += sigmav
                    P[w].append(v)
        delta = dict.fromkeys(nodes, 0.0)
        while S:
            w = S.pop()
            for v in P[w]:
                delta[v] += (sigma[v] / sigma[w]) * (1.0 + delta[w])
            if w != s:
                betweenness[w] += delta[w]
    return betweenness

这段代码实现了与 `nx.betweenness_centrality` 函数类似的功能，计算了给定图 `G` 中所有节点的中介中心性，返回一个字典，其中键是节点，值是其对应的中介中心性。