我们可以使用 NetworkX 中的closeness_centrality方法来计算紧密度中心性。 # 计算紧密度中心性closeness=nx.closeness_centrality(G)# 输出所有节点的紧密度中心性print("所有节点的紧密度中心性:",closeness) 1. 2. 3. 4. 5. 第一行代码计算了图中每个节点的紧密度中心性,并将结果存储在字典closeness中。第二...
# 需要导入模块: import networkx [as 别名] # 或者: from networkx import eigenvector_centrality [as 别名] def CentralityMeasures(G): # Betweenness centrality bet_cen = nx.betweenness_centrality(G) # Closeness centrality clo_cen = nx.closeness_centrality(G) # Eigenvector centrality eig_cen = nx...
Closeness centrality 量化一个节点到达图中所有其他节点的速度。具有较高接近中心性的节点被认为更具中心性，因为它们可以更有效地与其他节点进行通信。closeness_centrality = nx.closeness_centrality(G)for node, centrality in closeness_centrality.items():print(f'Closeness Centrality of {node}: {centrality:.2f...
closeness_centrality = nx.closeness_centrality(G)fornode, centralityincloseness_centrality.items():print(f'Closeness Centrality of{node}:{centrality:.2f}') 可视化 # Calculate centrality measuresdegree_centrality = nx.degree_centrality(G)...
# Closeness centrality plt.subplot(133) nx.draw(G, pos, with_labels=True, font_size=10, node_size=[v * 3000 for v in closeness_centrality.values()], node_color=list(closeness_centrality.values()), cmap=plt.cm.Greens, edge_color='gray', alpha=0.6) ...
centrality_df.sort_values(by=['Betweenness', 'Closeness', 'Degree', 'Eigenvector'], ascending=False, inplace=True) # 将结果导出到 Excel 文件,可以观察每个主体或因子的集聚性 centrality_df.to_excel('中心性指标排序.xlsx', index=False)
# Closeness centrality plt.subplot(133) nx.draw(G, pos, with_labels=True, font_size=10, node_size=[v * 3000 for v in closeness_centrality.values()], node_color=list(closeness_centrality.values()), cmap=plt.cm.Greens, edge_color='gray', alpha=0.6) ...
# Closeness centrality plt.subplot(133) nx.draw(G, pos, with_labels=True, font_size=10, node_size=[v * 3000 for v in closeness_centrality.values()], node_color=list(closeness_centrality.values()), cmap=plt.cm.Greens, edge_color='gray', alpha=0.6) ...
for node, centrality in closeness_centrality.items(): print(f'Closeness Centrality of {node}: {centrality:.2f}') 可视化 # Calculate centrality measures degree_centrality = nx.degree_centrality(G) betweenness_centrality = nx.betweenness_centrality(G) ...
# Closeness centrality plt.subplot(133) nx.draw(G, pos,with_labels=True,font_size=10, node_size=[v * 3000forvincloseness_centrality.values()],node_color=list(closeness_centrality.values()),cmap=plt.cm.Greens,edge_color='gray',alpha=0.6) ...