Python 如何使用networkX检测有向加权图中的社区？

我有一个有向加权图，一个连接的组件，希望检测社区

我试过了

community.best_partition(G)

但是，要获取分区，我会得到类型错误：

错误的图形类型，请仅使用非定向图形

我把我的图转换成无向图，然后得到了分区。这是一种解决问题的方法，而无需将定向转换为无向并同时使用权重吗

此外，我还有一个问题，我如何才能在每个分区社区中获得前5个中心度节点？例如：

undirected_strong_partition = community.best_partition(undirectedstrong_community,weight='weight')
undirected_strong_partition

{'node1': 0,
 'node2': 0,
 'node3': 0,
 'node4': 0,
 'node5': 1,
 'node6': 2,
 'node7': 2,
 'node8': 2,
 'node9': 2,
...}

如果可能的话，我想在每个社区中获得前5个度节点。例如：

{community0: [nodetop1,nodetop2...nodetop5]}

---

这可以通过以下方式解决：

代码：

import community
import networkx as nx

# Generate test graph
G = nx.fast_gnp_random_graph(100, 0.1)

# Relabel nodes
G = nx.relabel_nodes(G, {i: f"node_{i}" for i in G.nodes})

# Compute partition
partition = community.best_partition(G)

# Get a set of the communities
communities = set(partition.values())

# Create a dictionary mapping community number to nodes within that community
communities_dict = {c: [k for k, v in partition.items() if v == c] for c in communities}

# Filter that dictionary to map to first sort the nodes in the community by degree, then take the top 5.
highest_degree = {k: sorted(v, key=lambda x: G.degree(x))[-5:] for k, v in communities_dict.items()}

>>> highest_degree
{0: ['node_91', 'node_24', 'node_19', 'node_8', 'node_83'],
 1: ['node_54', 'node_69', 'node_88', 'node_48', 'node_84'],
 2: ['node_79', 'node_34', 'node_52', 'node_46', 'node_36'],
 3: ['node_98', 'node_96', 'node_86', 'node_76', 'node_30'],
 4: ['node_29', 'node_40', 'node_10', 'node_90', 'node_32'],
 5: ['node_94', 'node_97', 'node_59', 'node_25', 'node_37'],
 6: ['node_31', 'node_56', 'node_57', 'node_62', 'node_63']}

输出：

import community
import networkx as nx

# Generate test graph
G = nx.fast_gnp_random_graph(100, 0.1)

# Relabel nodes
G = nx.relabel_nodes(G, {i: f"node_{i}" for i in G.nodes})

# Compute partition
partition = community.best_partition(G)

# Get a set of the communities
communities = set(partition.values())

# Create a dictionary mapping community number to nodes within that community
communities_dict = {c: [k for k, v in partition.items() if v == c] for c in communities}

# Filter that dictionary to map to first sort the nodes in the community by degree, then take the top 5.
highest_degree = {k: sorted(v, key=lambda x: G.degree(x))[-5:] for k, v in communities_dict.items()}

>>> highest_degree
{0: ['node_91', 'node_24', 'node_19', 'node_8', 'node_83'],
 1: ['node_54', 'node_69', 'node_88', 'node_48', 'node_84'],
 2: ['node_79', 'node_34', 'node_52', 'node_46', 'node_36'],
 3: ['node_98', 'node_96', 'node_86', 'node_76', 'node_30'],
 4: ['node_29', 'node_40', 'node_10', 'node_90', 'node_32'],
 5: ['node_94', 'node_97', 'node_59', 'node_25', 'node_37'],
 6: ['node_31', 'node_56', 'node_57', 'node_62', 'node_63']}

---

你能解释一下这是怎么回事吗？理想情况下，为什么

community.best_partition（）

返回该错误？您可以回答无向图的问题。但是OP有一个有向图。