城市社区划分网络可视化异常：现有代码输出不符合预期需求

数据背景 #

• 拥有按年份划分社区的城市网络图，各年份的节点社区分配存储在独立数据集（如community2000对应2000年）
• 图G的节点示例：

{'Node': {0: 'albany', 1: 'almaty', 2: 'amsterdam'}}

• 节点间边示例（以albany为例）：

{'Source': {0: 'albany',
  1: 'albany',
  2: 'albany',
  3: 'albany',
  4: 'albany',
  5: 'albany',
  6: 'albany',
  7: 'albany',
  8: 'albany',
  9: 'albany'},
 'Target': {0: 'almaty',
  1: 'amsterdam',
  2: 'ankara',
  3: 'athens',
  4: 'atlanta',
  5: 'auckland',
  6: 'austin',
  7: 'bangalore',
  8: 'bangkok',
  9: 'barcelona'}}

• 已通过以下代码为图G的每个节点添加Community属性：

for node in G.nodes():
    city = node
    community = community_df.loc[community_df['city'] == city, 'cluster'+community_filename[-4:]].values[0]
    G.nodes[node]['Community'] = community

当前问题 #

使用以下代码绘制网络图时，输出的图混乱不堪，无法区分社区，不符合预期：

import matplotlib.pyplot as plt

# Create a dictionary to map community labels to unique integers
community_labels = {}
next_community_label = 0

# Assign a unique integer label to each community
for node in G.nodes():
    community = G.nodes[node]['Community']
    if community not in community_labels:
        community_labels[community] = next_community_label
        next_community_label += 1

# Create a dictionary to map cluster labels to unique integers within each community
cluster_labels = {}

# Remove self-loops
G.remove_edges_from(nx.selfloop_edges(G))

# Draw the network graph
pos = nx.spring_layout(G, k=0.3)  # Layout algorithm for node positioning
plt.figure(figsize=(12, 8))

for community in community_labels.values():
    nodes = [node for node, attr in G.nodes(data=True) if attr['Community'] == community]
    
    # Assign cluster labels within each community
    for node in nodes:
        cluster = community_df.loc[community_df['city'] == node, 'cluster' + community_filename[-4:]].values[0]
        cluster_labels[node] = cluster
    
    node_colors = [cluster_labels[node] for node in nodes]
    
    nx.draw_networkx_nodes(G, pos, nodelist=nodes, node_size=200, node_color=node_colors, cmap='viridis')
    nx.draw_networkx_edges(G, pos, edgelist=G.edges, alpha=0.1, width=0.5)
    nx.draw_networkx_labels(G, pos, font_size=8, font_color='black', labels={node: node for node in nodes})

plt.axis('off')
plt.title("Network of Cities Divided into Communities and Clusters")
plt.tight_layout()
plt.show()

期望效果 #

生成带城市名称节点标签的清晰分社区可视化图，能够明确区分不同社区。

内容的提问来源于stack exchange，提问作者Lusian