python-3.x - 特征向量中心性numpy的不同结果

Question

以下示例给出了使用 eigenvector_centrality 获得的不同结果和 eigenvector_centrality_numpy .有没有办法让这种计算更加健壮？我正在使用 networkx 2.4 , numpy 1.18.5和 scipy 1.5.0 .

import numpy as np
import networkx as nx

AdjacencyMatrix = {
    0: {
        1: 0.6,
    },
    1: {
        2: 0,
        3: 0,
    },
    2: {
        4: 0.5,
        5: 0.5,
    },
    3: {
        6: 0.5,
        7: 0.5,
        8: 0.5,
    },
    4: {},
    5: {},
    6: {},
    7: {},
    8: {},
}

G = nx.DiGraph()

for nodeID in AdjacencyMatrix.keys():
    G.add_node(nodeID)

for k1 in AdjacencyMatrix.keys():
    for k2 in AdjacencyMatrix[k1]:
        weight = AdjacencyMatrix[k1][k2]
        split_factor = len(AdjacencyMatrix[k1])
        G.add_edge(k1, k2, weight=weight / split_factor, reciprocal=1.0 / (split_factor * weight) if weight != 0 else np.inf)

eigenvector_centrality = {v[0]: v[1] for v in sorted(nx.eigenvector_centrality(G.reverse() if G.is_directed() else G, max_iter=10000, weight="weight").items(), key=lambda x: x[1], reverse=True)}
print(eigenvector_centrality)

eigenvector_centrality_numpy = {v[0]: v[1] for v in sorted(nx.eigenvector_centrality_numpy(G.reverse() if G.is_directed() else G, max_iter=10000, weight="weight").items(), key=lambda x: x[1], reverse=True)}
print(eigenvector_centrality_numpy)

这是我的输出:

{0: 0.6468489798823026, 3: 0.5392481399595738, 2: 0.5392481399595732, 1: 0.0012439403459275048, 4: 0.0012439403459275048, 5: 0.0012439403459275048, 6: 0.0012439403459275048, 7: 0.0012439403459275048, 8: 0.0012439403459275048}
{3: 0.9637027924175013, 0: 0.0031436862826891288, 6: 9.593026373266866e-11, 8: 3.5132785569658154e-11, 4: 1.2627565659784068e-11, 1: 9.433263632036004e-14, 7: -2.6958851817582286e-11, 5: -3.185304797703736e-11, 2: -0.26695888283266833}

Answer 1

Joel 说 eigenvector_centrality 不是有向无环图的有用度量是正确的。见 this nice description of centrality .这对于代码的 numpy 和非 numpy 版本应该是无用的。