networkx官方文档里的代码运行报错，求指导

龖釋

<blockquote>import networkx as nx

运行时报错：

 b = seed.choice(list(G.neighbors(a)))
AttributeError: 'NoneType' object has no attribute 'choice'

代码是networkx官方文档里的代码，求指导怎么解决，官方文档链接https://networkx.org/documentation/latest/_modules/networkx/algorithms/smallworld.html#random_reference

龖釋

import networkx as nx
import numpy as np

def random_reference(G, niter=1, connectivity=True, seed=None):
    if G.is_directed():
        msg = "random_reference() not defined for directed graphs."
        raise nx.NetworkXError(msg)
    if len(G) < 4:
        raise nx.NetworkXError("Graph has less than four nodes.")

    from networkx.utils import cumulative_distribution, discrete_sequence

    local_conn = nx.connectivity.local_edge_connectivity

    G = G.copy()
    keys, degrees = zip(*G.degree())  # keys, degree
    cdf = cumulative_distribution(degrees)  # cdf of degree
    nnodes = len(G)
    nedges = nx.number_of_edges(G)
    niter = niter * nedges
    ntries = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2))
    swapcount = 0

    for i in range(niter):
        n = 0
        while n < ntries:
            # pick two random edges without creating edge list
            # choose source node indices from discrete distribution
            (ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed)
            if ai == ci:
                continue  # same source, skip
            a = keys[ai]  # convert index to label
            c = keys[ci]
            # choose target uniformly from neighbors
            b = seed.choice(list(G.neighbors(a)))
            d = seed.choice(list(G.neighbors(c)))
            bi = keys.index(b)
            di = keys.index(d)
            if b in [a, c, d] or d in [a, b, c]:
                continue  # all vertices should be different

            # don't create parallel edges
            if (d not in G[a]) and (b not in G[c]):
                G.add_edge(a, d)
                G.add_edge(c, b)
                G.remove_edge(a, b)
                G.remove_edge(c, d)

                # Check if the graph is still connected
                if connectivity and local_conn(G, a, b) == 0:
                    # Not connected, revert the swap
                    G.remove_edge(a, d)
                    G.remove_edge(c, b)
                    G.add_edge(a, b)
                    G.add_edge(c, d)
                else:
                    swapcount += 1
                    break
            n += 1
    return G

def sigma(G, niter=100, nrand=10, seed=None):
    randMetrics = {"C": [], "L": []}
    for i in range(nrand):
        Gr = random_reference(G, niter=niter, seed=seed)
        randMetrics["C"].append(nx.transitivity(Gr))
        randMetrics["L"].append(nx.average_shortest_path_length(Gr))

    C = nx.transitivity(G)
    L = nx.average_shortest_path_length(G)
    Cr = np.mean(randMetrics["C"])
    Lr = np.mean(randMetrics["L"])

    sigma = (C / Cr) / (L / Lr)

    return sigma

if __name__ == "__main__":
    print(sigma(G))

suchocolate

代码不全，G没有实际值，seed也没传入实际内容。
给的链接也只是码源，并没有demo，最后按官方demo来。