Networkx 中 Louvain 分区的可视化

Question

请帮我改变可视化Louvain聚类算法的结果。我从网站上拿了代码 https://github.com/taynaud/python-louvain 我可以重写代码，让每个簇都有自己的形状（圆形、三角形、方形……）吗？

import community as community_louvain
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import networkx as nx

# load the karate club graph
G = nx.karate_club_graph()

# compute the best partition
partition = community_louvain.best_partition(G)

# 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_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

不幸的是nx.draw_networkx_nodes does not accept an iterable of shapes, so you'll have to loop over the nodes and plot them individually. Also, we'll have to index the generated cmap, otherwise, the single valued community values will get mapped to the same initial cmap color. For the possible shapes I'm just replicating the string of available shapes mentioned in the docs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])

Networkx 中 Louvain 分区的可视化

Visualization of Louvain partitions in Networkx

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