Networkx 中 Louvain 分区的可视化

Question

请帮助我更改可视化 Louvain 聚类算法的结果。我从网站 https://github.com/taynaud/python-louvain获取代码 我可以重写代码以便每个集群都有自己的形状（圆形、三角形、正方形……）吗？import community as community_louvainimport matplotlib.cm as cmimport matplotlib.pyplot as pltimport networkx as nx# load the karate club graphG = nx.karate_club_graph()# compute the best partitionpartition = community_louvain.best_partition(G)# draw the graphpos = nx.spring_layout(G)# color the nodes according to their partitioncmap = cm.get_cmap('viridis', max(partition.values()) + 1)nx.draw_networkx_nodes(G, pos, partition.keys(), node_size=40,                       cmap=cmap, node_color=list(partition.values()))nx.draw_networkx_edges(G, pos, alpha=0.5)plt.show()

Answer 1

不幸的nx.draw_networkx_nodes是不接受可迭代的形状，因此您必须遍历节点并单独绘制它们。此外，我们必须索引生成的cmap，否则，单值社区值将映射到相同的初始 cmap 颜色。对于可能的形状，我只是复制文档中提到的可用形状的字符串，并根据分区号对其进行索引：

# load the karate club graph

G = nx.karate_club_graph()

# compute the best partition

partition = community_louvain.best_partition(G)

cmap = cm.get_cmap('viridis', max(partition.values()) + 1)

shapes = 'so^>v<dph8'

plt.figure(figsize=(12,8))

# draw the graph

pos = nx.spring_layout(G)

# color the nodes according to their partition

cmap = cm.get_cmap('viridis', max(partition.values()) + 1)

nx.draw_networkx_edges(G, pos, alpha=0.5)

for node, color in partition.items():

    nx.draw_networkx_nodes(G, pos, [node], node_size=100,

                           node_color=[cmap.colors[color]],

                           node_shape=shapes[color])